Publications | Max Planck Institute for Intelligent Systems

442 results (View BibTeX file of all listed publications)

2006

Global Biclustering of Microarray Data

Wolf, T., Brors, B., Hofmann, T., Georgii, E.

In ICDMW 2006, pages: 125-129, (Editors: Tsumoto, S. , C. W. Clifton, N. Zhong, X. Wu, J. Liu, B. W. Wah, Y.-M. Cheung), IEEE Computer Society, Los Alamitos, CA, USA, Sixth IEEE International Conference on Data Mining, December 2006 (inproceedings)

Abstract

We consider the problem of simultaneously clustering genes and conditions of a gene expression data matrix. A bicluster is defined as a subset of genes that show similar behavior within a subset of conditions. Finding biclusters can be useful for revealing groups of genes involved in the same molecular process as well as groups of conditions where this process takes place. Previous work either deals with local, bicluster-based criteria or assumes a very specific structure of the data matrix (e.g. checkerboard or block-diagonal) [11]. In contrast, our goal is to find a set of flexibly arranged biclusters which is optimal in regard to a global objective function. As this is a NP-hard combinatorial problem, we describe several techniques to obtain approximate solutions. We benchmarked our approach successfully on the Alizadeh B-cell lymphoma data set [1].

Web DOI [BibTex]

2006

ei Wolf, T., Brors, B., Hofmann, T., Georgii, E. Global Biclustering of Microarray Data In ICDMW 2006, pages: 125-129, (Editors: Tsumoto, S. , C. W. Clifton, N. Zhong, X. Wu, J. Liu, B. W. Wah, Y.-M. Cheung), IEEE Computer Society, Los Alamitos, CA, USA, Sixth IEEE International Conference on Data Mining, December 2006 (inproceedings)

Web DOI [BibTex]

Conformal Multi-Instance Kernels

Blaschko, M., Hofmann, T.

In NIPS 2006 Workshop on Learning to Compare Examples, pages: 1-6, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

Abstract

In the multiple instance learning setting, each observation is a bag of feature vectors of which one or more vectors indicates membership in a class. The primary task is to identify if any vectors in the bag indicate class membership while ignoring vectors that do not. We describe here a kernel-based technique that defines a parametric family of kernels via conformal transformations and jointly learns a discriminant function over bags together with the optimal parameter settings of the kernel. Learning a conformal transformation effectively amounts to weighting regions in the feature space according to their contribution to classification accuracy; regions that are discriminative will be weighted higher than regions that are not. This allows the classifier to focus on regions contributing to classification accuracy while ignoring regions that correspond to vectors found both in positive and in negative bags. We show how parameters of this transformation can be learned for support vector machines by posing the problem as a multiple kernel learning problem. The resulting multiple instance classifier gives competitive accuracy for several multi-instance benchmark datasets from different domains.

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ei Blaschko, M., Hofmann, T. Conformal Multi-Instance Kernels In NIPS 2006 Workshop on Learning to Compare Examples, pages: 1-6, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

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Information-theoretic Metric Learning

Davis, J., Kulis, B., Sra, S., Dhillon, I.

In NIPS 2006 Workshop on Learning to Compare Examples, pages: 1-5, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

Abstract

We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the Mahalanobis distance function. Via a surprising equivalence, we show that this problem can be solved as a low-rank kernel learning problem. Specifically, we minimize the Burg divergence of a low-rank kernel to an input kernel, subject to pairwise distance constraints. Our approach has several advantages over existing methods. First, we present a natural information-theoretic formulation for the problem. Second, the algorithm utilizes the methods developed by Kulis et al. [6], which do not involve any eigenvector computation; in particular, the running time of our method is faster than most existing techniques. Third, the formulation offers insights into connections between metric learning and kernel learning.

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ei Davis, J., Kulis, B., Sra, S., Dhillon, I. Information-theoretic Metric Learning In NIPS 2006 Workshop on Learning to Compare Examples, pages: 1-5, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

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Pattern Mining in Frequent Dynamic Subgraphs

Borgwardt, KM., Kriegel, H-P., Wackersreuther, P.

In pages: 818-822, (Editors: Clifton, C.W.), IEEE Computer Society, Los Alamitos, CA, USA, Sixth International Conference on Data Mining (ICDM), December 2006 (inproceedings)

Abstract

Graph-structured data is becoming increasingly abundant in many application domains. Graph mining aims at finding interesting patterns within this data that represent novel knowledge. While current data mining deals with static graphs that do not change over time, coming years will see the advent of an increasing number of time series of graphs. In this article, we investigate how pattern mining on static graphs can be extended to time series of graphs. In particular, we are considering dynamic graphs with edge insertions and edge deletions over time. We define frequency in this setting and provide algorithmic solutions for finding frequent dynamic subgraph patterns. Existing subgraph mining algorithms can be easily integrated into our framework to make them handle dynamic graphs. Experimental results on real-world data confirm the practical feasibility of our approach.

Web DOI [BibTex]

ei Borgwardt, KM., Kriegel, H-P., Wackersreuther, P. Pattern Mining in Frequent Dynamic Subgraphs In pages: 818-822, (Editors: Clifton, C.W.), IEEE Computer Society, Los Alamitos, CA, USA, Sixth International Conference on Data Mining (ICDM), December 2006 (inproceedings)

Web DOI [BibTex]

Structure validation of the Josephin domain of ataxin-3: Conclusive evidence for an open conformation

Nicastro, G., Habeck, M., Masino, L., Svergun, DI., Pastore, A.

Journal of Biomolecular NMR, 36(4):267-277, December 2006 (article)

Abstract

The availability of new and fast tools in structure determination has led to a more than exponential growth of the number of structures solved per year. It is therefore increasingly essential to assess the accuracy of the new structures by reliable approaches able to assist validation. Here, we discuss a specific example in which the use of different complementary techniques, which include Bayesian methods and small angle scattering, resulted essential for validating the two currently available structures of the Josephin domain of ataxin-3, a protein involved in the ubiquitin/proteasome pathway and responsible for neurodegenerative spinocerebellar ataxia of type 3. Taken together, our results demonstrate that only one of the two structures is compatible with the experimental information. Based on the high precision of our refined structure, we show that Josephin contains an open cleft which could be directly implicated in the interaction with polyubiquitin chains and other partners.

Web DOI [BibTex]

ei Nicastro, G., Habeck, M., Masino, L., Svergun, DI., Pastore, A. Structure validation of the Josephin domain of ataxin-3: Conclusive evidence for an open conformation Journal of Biomolecular NMR, 36(4):267-277, December 2006 (article)

Web DOI [BibTex]

A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression

Franz, M., Schölkopf, B.

Neural Computation, 18(12):3097-3118, December 2006 (article)

Abstract

Volterra and Wiener series are perhaps the best understood nonlinear system representations in signal processing. Although both approaches have enjoyed a certain popularity in the past, their application has been limited to rather low-dimensional and weakly nonlinear systems due to the exponential growth of the number of terms that have to be estimated. We show that Volterra and Wiener series can be represented implicitly as elements of a reproducing kernel Hilbert space by utilizing polynomial kernels. The estimation complexity of the implicit representation is linear in the input dimensionality and independent of the degree of nonlinearity. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled.

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ei Franz, M., Schölkopf, B. A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression Neural Computation, 18(12):3097-3118, December 2006 (article)

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Induced Master Motion in Force-Reflecting Teleoperation

Kuchenbecker, K. J., Niemeyer, G.

ASME Journal of Dynamic Systems, Measurement, and Control, 128(4):800-810, December 2006 (article)

[BibTex]

hi Kuchenbecker, K. J., Niemeyer, G. Induced Master Motion in Force-Reflecting Teleoperation ASME Journal of Dynamic Systems, Measurement, and Control, 128(4):800-810, December 2006 (article)

[BibTex]

3DString: a feature string kernel for 3D object classification on voxelized data

Assfalg, J., Borgwardt, KM., Kriegel, H-P.

In pages: 198-207, (Editors: Yu, P.S. , V.J. Tsotras, E.A. Fox, B. Liu), ACM Press, New York, NY, USA, 15th ACM International Conference on Information and Knowledge Management (CIKM), November 2006 (inproceedings)

Abstract

Classification of 3D objects remains an important task in many areas of data management such as engineering, medicine or biology. As a common preprocessing step in current approaches to classification of voxelized 3D objects, voxel representations are transformed into a feature vector description.In this article, we introduce an approach of transforming 3D objects into feature strings which represent the distribution of voxels over the voxel grid. Attractively, this feature string extraction can be performed in linear runtime with respect to the number of voxels. We define a similarity measure on these feature strings that counts common k-mers in two input strings, which is referred to as the spectrum kernel in the field of kernel methods. We prove that on our feature strings, this similarity measure can be computed in time linear to the number of different characters in these strings. This linear runtime behavior makes our kernel attractive even for large datasets that occur in many application domains. Furthermore, we explain that our similarity measure induces a metric which allows to combine it with an M-tree for handling of large volumes of data. Classification experiments on two published benchmark datasets show that our novel approach is competitive with the best state-of-the-art methods for 3D object classification.

DOI [BibTex]

ei Assfalg, J., Borgwardt, KM., Kriegel, H-P. 3DString: a feature string kernel for 3D object classification on voxelized data In pages: 198-207, (Editors: Yu, P.S. , V.J. Tsotras, E.A. Fox, B. Liu), ACM Press, New York, NY, USA, 15th ACM International Conference on Information and Knowledge Management (CIKM), November 2006 (inproceedings)

DOI [BibTex]

Spatial four-alternative forced-choice method is the preferred psychophysical method for naive observers

Jäkel, F., Wichmann, F.

Journal of Vision, 6(11):1307-1322, November 2006 (article)

PDF Web DOI [BibTex]

ei Jäkel, F., Wichmann, F. Spatial four-alternative forced-choice method is the preferred psychophysical method for naive observers Journal of Vision, 6(11):1307-1322, November 2006 (article)

PDF Web DOI [BibTex]

Prediction of Protein Function from Networks

Shin, H., Tsuda, K.

In Semi-Supervised Learning, pages: 361-376, Adaptive Computation and Machine Learning, (Editors: Chapelle, O. , B. Schölkopf, A. Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

Abstract

In computational biology, it is common to represent domain knowledge using graphs. Frequently there exist multiple graphs for the same set of nodes, representing information from different sources, and no single graph is sufficient to predict class labels of unlabelled nodes reliably. One way to enhance reliability is to integrate multiple graphs, since individual graphs are partly independent and partly complementary to each other for prediction. In this chapter, we describe an algorithm to assign weights to multiple graphs within graph-based semi-supervised learning. Both predicting class labels and searching for weights for combining multiple graphs are formulated into one convex optimization problem. The graph-combining method is applied to functional class prediction of yeast proteins.When compared with individual graphs, the combined graph with optimized weights performs significantly better than any single graph.When compared with the semidefinite programming-based support vector machine (SDP/SVM), it shows comparable accuracy in a remarkably short time. Compared with a combined graph with equal-valued weights, our method could select important graphs without loss of accuracy, which implies the desirable property of integration with selectivity.

Web [BibTex]

ei Shin, H., Tsuda, K. Prediction of Protein Function from Networks In Semi-Supervised Learning, pages: 361-376, Adaptive Computation and Machine Learning, (Editors: Chapelle, O. , B. Schölkopf, A. Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

Web [BibTex]

Adapting Spatial Filter Methods for Nonstationary BCIs

Tomioka, R., Hill, J., Blankertz, B., Aihara, K.

In IBIS 2006, pages: 65-70, 2006 Workshop on Information-Based Induction Sciences, November 2006 (inproceedings)

Abstract

A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the possible nonstationarity in the data from the datablock the method is trained on and that the method is applied to. Assuming the joint distributions of the whitened signal and the class label to be identical in two blocks, where the whitening is done in each block independently, we propose a simple adaptation formula that is applicable to a broad class of spatial filtering methods including ICA, CSP, and logistic regression classifiers. We characterize the class of linear transformations for which the above assumption holds. Experimental results on 60 BCI datasets show improved classification accuracy compared to (a) fixed spatial filter approach (no adaptation) and (b) fixed spatial pattern approach (proposed by Hill et al., 2006 [1]).

PDF [BibTex]

ei Tomioka, R., Hill, J., Blankertz, B., Aihara, K. Adapting Spatial Filter Methods for Nonstationary BCIs In IBIS 2006, pages: 65-70, 2006 Workshop on Information-Based Induction Sciences, November 2006 (inproceedings)

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Discrete Regularization

Zhou, D., Schölkopf, B.

In Semi-supervised Learning, pages: 237-250, Adaptive computation and machine learning, (Editors: O Chapelle and B Schölkopf and A Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

Abstract

Many real-world machine learning problems are situated on finite discrete sets, including dimensionality reduction, clustering, and transductive inference. A variety of approaches for learning from finite sets has been proposed from different motivations and for different problems. In most of those approaches, a finite set is modeled as a graph, in which the edges encode pairwise relationships among the objects in the set. Consequently many concepts and methods from graph theory are adopted. In particular, the graph Laplacian is widely used. In this chapter we present a systemic framework for learning from a finite set represented as a graph. We develop discrete analogues of a number of differential operators, and then construct a discrete analogue of classical regularization theory based on those discrete differential operators. The graph Laplacian based approaches are special cases of this general discrete regularization framework. An important thing implied in this framework is that we have a wide choices of regularization on graph in addition to the widely-used graph Laplacian based one.

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ei Zhou, D., Schölkopf, B. Discrete Regularization In Semi-supervised Learning, pages: 237-250, Adaptive computation and machine learning, (Editors: O Chapelle and B Schölkopf and A Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

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Statistical Analysis of Slow Crack Growth Experiments

Pfingsten, T., Glien, K.

Journal of the European Ceramic Society, 26(15):3061-3065, November 2006 (article)

Abstract

A common approach for the determination of Slow Crack Growth (SCG) parameters are the static and dynamic loading method. Since materials with small Weibull module show a large variability in strength, a correct statistical analysis of the data is indispensable. In this work we propose the use of the Maximum Likelihood method and a Baysian analysis, which, in contrast to the standard procedures, take into account that failure strengths are Weibull distributed. The analysis provides estimates for the SCG parameters, the Weibull module, and the corresponding confidence intervals and overcomes the necessity of manual differentiation between inert and fatigue strength data. We compare the methods to a Least Squares approach, which can be considered the standard procedure. The results for dynamic loading data from the glass sealing of MEMS devices show that the assumptions inherent to the standard approach lead to significantly different estimates.

PDF PDF DOI [BibTex]

ei Pfingsten, T., Glien, K. Statistical Analysis of Slow Crack Growth Experiments Journal of the European Ceramic Society, 26(15):3061-3065, November 2006 (article)

PDF PDF DOI [BibTex]

An Improved Adaptive Power Line Interference Canceller for Electrocardiography

Martens, SMM., Mischi, M., Oei, SG., Bergmans, JWM.

IEEE Transactions on Biomedical Engineering, 53(11):2220-2231, November 2006 (article)

Abstract

Power line interference may severely corrupt a biomedical recording. Notch filters and adaptive cancellers have been suggested to suppress this interference. We propose an improved adaptive canceller for the reduction of the fundamental power line interference component and harmonics in electrocardiogram (ECG) recordings. The method tracks the amplitude, phase, and frequency of all the interference components for power line frequency deviations up to about 4 Hz. A comparison is made between the performance of our method, former adaptive cancellers, and a narrow and a wide notch filter in suppressing the fundamental power line interference component. For this purpose a real ECG signal is corrupted by an artificial power line interference signal. The cleaned signal after applying all methods is compared with the original ECG signal. Our improved adaptive canceller shows a signal-to-power-line-interference ratio for the fundamental component up to 30 dB higher than that produced by the other methods. Moreover, our method is also effective for the suppression of the harmonics of the power line interference.

Web DOI [BibTex]

ei Martens, SMM., Mischi, M., Oei, SG., Bergmans, JWM. An Improved Adaptive Power Line Interference Canceller for Electrocardiography IEEE Transactions on Biomedical Engineering, 53(11):2220-2231, November 2006 (article)

Web DOI [BibTex]

Donagi-Markman cubic for Hitchin systems

Balduzzi, D.

Mathematical Research Letters, 13(6):923-933, November 2006 (article)

Abstract

The Donagi-Markman cubic is the differential of the period map for algebraic completely integrable systems. Here we prove a formula for the cubic in the case of Hitchin’s system for arbitrary semisimple g. This was originally stated (without proof) by Pantev for sln.

Web [BibTex]

ei Balduzzi, D. Donagi-Markman cubic for Hitchin systems Mathematical Research Letters, 13(6):923-933, November 2006 (article)

Web [BibTex]

Mining frequent stem patterns from unaligned RNA sequences

Hamada, M., Tsuda, K., Kudo, T., Kin, T., Asai, K.

Bioinformatics, 22(20):2480-2487, October 2006 (article)

Abstract

Motivation: In detection of non-coding RNAs, it is often necessary to identify the secondary structure motifs from a set of putative RNA sequences. Most of the existing algorithms aim to provide the best motif or few good motifs, but biologists often need to inspect all the possible motifs thoroughly. Results: Our method RNAmine employs a graph theoretic representation of RNA sequences, and detects all the possible motifs exhaustively using a graph mining algorithm. The motif detection problem boils down to finding frequently appearing patterns in a set of directed and labeled graphs. In the tasks of common secondary structure prediction and local motif detection from long sequences, our method performed favorably both in accuracy and in efficiency with the state-of-the-art methods such as CMFinder.

PDF Web DOI [BibTex]

ei Hamada, M., Tsuda, K., Kudo, T., Kin, T., Asai, K. Mining frequent stem patterns from unaligned RNA sequences Bioinformatics, 22(20):2480-2487, October 2006 (article)

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Large-Scale Gene Expression Profiling Reveals Major Pathogenetic Pathways of Cartilage Degeneration in Osteoarthritis

Aigner, T., Fundel, K., Saas, J., Gebhard, P., Haag, J., Weiss, T., Zien, A., Obermayr, F., Zimmer, R., Bartnik, E.

Arthritis and Rheumatism, 54(11):3533-3544, October 2006 (article)

Abstract

Objective. Despite many research efforts in recent decades, the major pathogenetic mechanisms of osteo- arthritis (OA), including gene alterations occurring during OA cartilage degeneration, are poorly under- stood, and there is no disease-modifying treatment approach. The present study was therefore initiated in order to identify differentially expressed disease-related genes and potential therapeutic targets. Methods. This investigation consisted of a large gene expression profiling study performed based on 78 normal and disease samples, using a custom-made complementar y DNA array covering >4,000 genes. Results. Many differentially expressed genes were identified, including the expected up-regulation of ana- bolic and catabolic matrix genes. In particular, the down-regulation of important oxidative defense genes, i.e., the genes for superoxide dismutases 2 and 3 and glutathione peroxidase 3, was prominent. This indicates that continuous oxidative stress to the cells and the matrix is one major underlying pathogenetic mecha- nism in OA. Also, genes that are involved in the phenot ypic stabilit y of cells, a feature that is greatly reduced in OA cartilage, appeared to be suppressed. Conclusion. Our findings provide a reference data set on gene alterations in OA cartilage and, importantly, indicate major mechanisms underlying central cell bio- logic alterations that occur during the OA disease process. These results identify molecular targets that can be further investigated in the search for therapeutic interventions.

Web DOI [BibTex]

ei Aigner, T., Fundel, K., Saas, J., Gebhard, P., Haag, J., Weiss, T., Zien, A., Obermayr, F., Zimmer, R., Bartnik, E. Large-Scale Gene Expression Profiling Reveals Major Pathogenetic Pathways of Cartilage Degeneration in Osteoarthritis Arthritis and Rheumatism, 54(11):3533-3544, October 2006 (article)

Web DOI [BibTex]

Extraction of visual features from natural video data using Slow Feature Analysis

Nickisch, H.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2006 (diplomathesis)

Abstract

Das Forschungsprojekt NeuRoBot hat das un{\"u}berwachte Erlernen einer neuronal inspirierten Steuerungsarchitektur zum Ziel, und zwar unter den Randbedingungen biologischer Plausibilit{\"a}t und der Benutzung einer Kamera als einzigen Sensor. Visuelle Merkmale, die ein angemessenes Abbild der Umgebung liefern, sind unerl{\"a}sslich, um das Ziel kollisionsfreier Navigation zu erreichen. Zeitliche Koh{\"a}renz ist ein neues Lernprinzip, das in der Lage ist, Erkenntnisse aus der Biologie des Sehens zu reproduzieren. Es wird durch die Beobachtung motiviert, dass die Sensoren der Retina auf deutlich k{\"u}rzeren Zeitskalen variieren als eine abstrakte Beschreibung. Zeitliche Langsamkeitsanalyse l{\"o}st das Problem, indem sie zeitlich langsam ver{\"a}nderliche Signale aus schnell ver{\"a}nderlichen Eingabesignalen extrahiert. Eine Verallgemeinerung auf Signale, die nichtlinear von den Eingaben abh{\"a}ngen, ist durch die Anwendung des Kernel-Tricks m{\"o}glich. Das einzig benutzte Vorwissen ist die zeitliche Glattheit der gewonnenen Signale. In der vorliegenden Diplomarbeit wird Langsamkeitsanalyse auf Bildausschnitte von Videos einer Roboterkamera und einer Simulationsumgebung angewendet. Zuallererst werden mittels Parameterexploration und Kreuzvalidierung die langsamst m{\"o}glichen Funktionen bestimmt. Anschließend werden die Merkmalsfunktionen analysiert und einige Ansatzpunkte f{\"u}r ihre Interpretation angegeben. Aufgrund der sehr großen Datens{\"a}tze und der umfangreichen Berechnungen behandelt ein Großteil dieser Arbeit auch Aufwandsbetrachtungen und Fragen der effizienten Berechnung. Kantendetektoren in verschiedenen Phasen und mit haupts{\"a}chlich horizontaler Orientierung stellen die wichtigsten aus der Analyse hervorgehenden Funktionen dar. Eine Anwendung auf konkrete Navigationsaufgaben des Roboters konnte bisher nicht erreicht werden. Eine visuelle Interpretation der erlernten Merkmale ist jedoch durchaus gegeben.

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ei Nickisch, H. Extraction of visual features from natural video data using Slow Feature Analysis Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2006 (diplomathesis)

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A Linear Programming Approach for Molecular QSAR analysis

Saigo, H., Kadowaki, T., Tsuda, K.

In MLG 2006, pages: 85-96, (Editors: Gärtner, T. , G. C. Garriga, T. Meinl), International Workshop on Mining and Learning with Graphs, September 2006, Best Paper Award (inproceedings)

Abstract

Small molecules in chemistry can be represented as graphs. In a quantitative structure-activity relationship (QSAR) analysis, the central task is to find a regression function that predicts the activity of the molecule in high accuracy. Setting a QSAR as a primal target, we propose a new linear programming approach to the graph-based regression problem. Our method extends the graph classification algorithm by Kudo et al. (NIPS 2004), which is a combination of boosting and graph mining. Instead of sequential multiplicative updates, we employ the linear programming boosting (LP) for regression. The LP approach allows to include inequality constraints for the parameter vector, which turns out to be particularly useful in QSAR tasks where activity values are sometimes unavailable. Furthermore, the efficiency is improved significantly by employing multiple pricing.

PDF PDF [BibTex]

ei Saigo, H., Kadowaki, T., Tsuda, K. A Linear Programming Approach for Molecular QSAR analysis In MLG 2006, pages: 85-96, (Editors: Gärtner, T. , G. C. Garriga, T. Meinl), International Workshop on Mining and Learning with Graphs, September 2006, Best Paper Award (inproceedings)

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Incremental Aspect Models for Mining Document Streams

Surendran, A., Sra, S.

In PKDD 2006, pages: 633-640, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Abstract

In this paper we introduce a novel approach for incrementally building aspect models, and use it to dynamically discover underlying themes from document streams. Using the new approach we present an application which we call query-line tracking i.e., we automatically discover and summarize different themes or stories that appear over time, and that relate to a particular query. We present evaluation on news corpora to demonstrate the strength of our method for both query-line tracking, online indexing and clustering.

Web DOI [BibTex]

ei Surendran, A., Sra, S. Incremental Aspect Models for Mining Document Streams In PKDD 2006, pages: 633-640, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Web DOI [BibTex]

Implicit Surface Modelling with a Globally Regularised Basis of Compact Support

Walder, C., Schölkopf, B., Chapelle, O.

Computer Graphics Forum, 25(3):635-644, September 2006 (article)

Abstract

We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The contributions of the paper include a novel means of regularising multi-scale compactly supported basis functions that leads to the desirable interpolation properties previously only associated with fully supported bases. We also provide a regularisation framework for simpler and more direct treatment of surface normals, along with a corresponding generalisation of the representer theorem lying at the core of kernel-based machine learning methods. We demonstrate the techniques on 3D problems of up to 14 million data points, as well as 4D time series data and four-dimensional interpolation between three-dimensional shapes.

PDF GZIP DOI [BibTex]

ei Walder, C., Schölkopf, B., Chapelle, O. Implicit Surface Modelling with a Globally Regularised Basis of Compact Support Computer Graphics Forum, 25(3):635-644, September 2006 (article)

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PALMA: Perfect Alignments using Large Margin Algorithms

Rätsch, G., Hepp, B., Schulze, U., Ong, C.

In GCB 2006, pages: 104-113, (Editors: Huson, D. , O. Kohlbacher, A. Lupas, K. Nieselt, A. Zell), Gesellschaft für Informatik, Bonn, Germany, German Conference on Bioinformatics, September 2006 (inproceedings)

Abstract

Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task. We present a novel approach based on large margin learning that combines kernel based splice site predictions with common sequence alignment techniques. By solving a convex optimization problem, our algorithm -- called PALMA -- tunes the parameters of the model such that the true alignment scores higher than all other alignments. In an experimental study on the alignments of mRNAs containing artificially generated micro-exons, we show that our algorithm drastically outperforms all other methods: It perfectly aligns all 4358 sequences on an hold-out set, while the best other method misaligns at least 90 of them. Moreover, our algorithm is very robust against noise in the query sequence: when deleting, inserting, or mutating up to 50% of the query sequence, it still aligns 95% of all sequences correctly, while other methods achieve less than 36% accuracy. For datasets, additional results and a stand-alone alignment tool see http://www.fml.mpg.de/raetsch/projects/palma.

PDF Web [BibTex]

ei Rätsch, G., Hepp, B., Schulze, U., Ong, C. PALMA: Perfect Alignments using Large Margin Algorithms In GCB 2006, pages: 104-113, (Editors: Huson, D. , O. Kohlbacher, A. Lupas, K. Nieselt, A. Zell), Gesellschaft für Informatik, Bonn, Germany, German Conference on Bioinformatics, September 2006 (inproceedings)

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Graph Based Semi-Supervised Learning with Sharper Edges

Shin, H., Hill, N., Rätsch, G.

In ECML 2006, pages: 401-412, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning (ECML), September 2006 (inproceedings)

Abstract

In many graph-based semi-supervised learning algorithms, edge weights are assumed to be fixed and determined by the data points&amp;amp;amp;lsquo; (often symmetric)relationships in input space, without considering directionality. However, relationships may be more informative in one direction (e.g. from labelled to unlabelled) than in the reverse direction, and some relationships (e.g. strong weights between oppositely labelled points) are unhelpful in either direction. Undesirable edges may reduce the amount of influence an informative point can propagate to its neighbours -- the point and its outgoing edges have been ``blunted.&amp;amp;amp;lsquo;&amp;amp;amp;lsquo; We present an approach to ``sharpening&amp;amp;amp;lsquo;&amp;amp;amp;lsquo; in which weights are adjusted to meet an optimization criterion wherever they are directed towards labelled points. This principle can be applied to a wide variety of algorithms. In the current paper, we present one ad hoc solution satisfying the principle, in order to show that it can improve performance on a number of publicly available benchmark data sets.

PDF Web DOI [BibTex]

ei Shin, H., Hill, N., Rätsch, G. Graph Based Semi-Supervised Learning with Sharper Edges In ECML 2006, pages: 401-412, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning (ECML), September 2006 (inproceedings)

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Robust MEG Source Localization of Event Related Potentials: Identifying Relevant Sources by Non-Gaussianity

Breun, P., Grosse-Wentrup, M., Utschick, W., Buss, M.

In DAGM 2006, pages: 394-403, (Editors: Franke, K. , K.-R. Müller, B. Nickolay, R. Schäfer), Springer, Berlin, Germany, 28th Annual Symposium of the German Association for Pattern Recognition, September 2006 (inproceedings)

Abstract

Independent Component Analysis (ICA) is a frequently used preprocessing step in source localization of MEG and EEG data. By decomposing the measured data into maximally independent components (ICs), estimates of the time course and the topographies of neural sources are obtained. In this paper, we show that when using estimated source topographies for localization, correlations between neural sources introduce an error into the obtained source locations. This error can be avoided by reprojecting ICs onto the observation space, but requires the identification of relevant ICs. For Event Related Potentials (ERPs), we identify relevant ICs by estimating their non-Gaussianity. The efficacy of the approach is tested on auditory evoked potentials (AEPs) recorded by MEG. It is shown that ten trials are sufficient for reconstructing all important characteristics of the AEP, and source localization of the reconstructed ERP yields the same focus of activity as the average of 250 trials.

PDF Web DOI [BibTex]

ei Breun, P., Grosse-Wentrup, M., Utschick, W., Buss, M. Robust MEG Source Localization of Event Related Potentials: Identifying Relevant Sources by Non-Gaussianity In DAGM 2006, pages: 394-403, (Editors: Franke, K. , K.-R. Müller, B. Nickolay, R. Schäfer), Springer, Berlin, Germany, 28th Annual Symposium of the German Association for Pattern Recognition, September 2006 (inproceedings)

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Semi-Supervised Learning

Chapelle, O., Schölkopf, B., Zien, A.

pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

Abstract

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

Web [BibTex]

ei Chapelle, O., Schölkopf, B., Zien, A. Semi-Supervised Learning pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

Web [BibTex]

Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle

Deisenroth, MP., Ohtsuka, T., Weissel, F., Brunn, D., Hanebeck, UD.

In MFI 2006, pages: 371-376, (Editors: Hanebeck, U. D.), IEEE Service Center, Piscataway, NJ, USA, 6th IEEE International Conference on Multisensor Fusion and Integration, September 2006 (inproceedings)

Abstract

In this paper, an approach to the finite-horizon optimal state-feedback control problem of nonlinear, stochastic, discrete-time systems is presented. Starting from the dynamic programming equation, the value function will be approximated by means of Taylor series expansion up to second-order derivatives. Moreover, the problem will be reformulated, such that a minimum principle can be applied to the stochastic problem. Employing this minimum principle, the optimal control problem can be rewritten as a two-point boundary-value problem to be solved at each time step of a shrinking horizon. To avoid numerical problems, the two-point boundary-value problem will be solved by means of a continuation method. Thus, the curse of dimensionality of dynamic programming is avoided, and good candidates for the optimal state-feedback controls are obtained. The proposed approach will be evaluated by means of a scalar example system.

PDF Web DOI [BibTex]

ei Deisenroth, MP., Ohtsuka, T., Weissel, F., Brunn, D., Hanebeck, UD. Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle In MFI 2006, pages: 371-376, (Editors: Hanebeck, U. D.), IEEE Service Center, Piscataway, NJ, USA, 6th IEEE International Conference on Multisensor Fusion and Integration, September 2006 (inproceedings)

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Uniform Convergence of Adaptive Graph-Based Regularization

Hein, M.

In COLT 2006, pages: 50-64, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory, September 2006 (inproceedings)

Abstract

The regularization functional induced by the graph Laplacian of a random neighborhood graph based on the data is adaptive in two ways. First it adapts to an underlying manifold structure and second to the density of the data-generating probability measure. We identify in this paper the limit of the regularizer and show uniform convergence over the space of Hoelder functions. As an intermediate step we derive upper bounds on the covering numbers of Hoelder functions on compact Riemannian manifolds, which are of independent interest for the theoretical analysis of manifold-based learning methods.

PDF Web DOI [BibTex]

ei Hein, M. Uniform Convergence of Adaptive Graph-Based Regularization In COLT 2006, pages: 50-64, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory, September 2006 (inproceedings)

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Efficient Large Scale Linear Programming Support Vector Machines

Sra, S.

In ECML 2006, pages: 767-774, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning, September 2006 (inproceedings)

Abstract

This paper presents a decomposition method for efficiently constructing ℓ1-norm Support Vector Machines (SVMs). The decomposition algorithm introduced in this paper possesses many desirable properties. For example, it is provably convergent, scales well to large datasets, is easy to implement, and can be extended to handle support vector regression and other SVM variants. We demonstrate the efficiency of our algorithm by training on (dense) synthetic datasets of sizes up to 20 million points (in ℝ32). The results show our algorithm to be several orders of magnitude faster than a previously published method for the same task. We also present experimental results on real data setsour method is seen to be not only very fast, but also highly competitive against the leading SVM implementations.

Web DOI [BibTex]

ei Sra, S. Efficient Large Scale Linear Programming Support Vector Machines In ECML 2006, pages: 767-774, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning, September 2006 (inproceedings)

Web DOI [BibTex]

Regularised CSP for Sensor Selection in BCI

Farquhar, J., Hill, N., Lal, T., Schölkopf, B.

In Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006, pages: 14-15, (Editors: GR Müller-Putz and C Brunner and R Leeb and R Scherer and A Schlögl and S Wriessnegger and G Pfurtscheller), Verlag der Technischen Universität Graz, Graz, Austria, 3rd International Brain-Computer Interface Workshop and Training Course, September 2006 (inproceedings)

Abstract

The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification performance considerably, but demands that a large number of electrodes be mounted, which is inconvenient in day-to-day BCI usage. The CSP algorithm is also known for its tendency to overfit, i.e. to learn the noise in the training set rather than the signal. Both problems motivate an approach in which spatial filters are sparsified. We briefly sketch a reformulation of the problem which allows us to do this, using 1-norm regularisation. Focusing on the electrode selection issue, we present preliminary results on EEG data sets that suggest that effective spatial filters may be computed with as few as 10--20 electrodes, hence offering the potential to simplify the practical realisation of BCI systems significantly.

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ei Farquhar, J., Hill, N., Lal, T., Schölkopf, B. Regularised CSP for Sensor Selection in BCI In Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006, pages: 14-15, (Editors: GR Müller-Putz and C Brunner and R Leeb and R Scherer and A Schlögl and S Wriessnegger and G Pfurtscheller), Verlag der Technischen Universität Graz, Graz, Austria, 3rd International Brain-Computer Interface Workshop and Training Course, September 2006 (inproceedings)

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Time-Dependent Demixing of Task-Relevant EEG Signals

Hill, N., Farquhar, J., Lal, T., Schölkopf, B.

In Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006, pages: 20-21, (Editors: GR Müller-Putz and C Brunner and R Leeb and R Scherer and A Schlögl and S Wriessnegger and G Pfurtscheller), Verlag der Technischen Universität Graz, Graz, Austria, 3rd International Brain-Computer Interface Workshop and Training Course, September 2006 (inproceedings)

Abstract

Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial *patterns* fixed rather than spatial filters, when transferring from training to test sessions or from one time window to another. We show that a fixed spatial pattern (FSP) approach, using a moving-window estimate of signal covariances, can be more robust to non-stationarity than a fixed spatial filter (FSF) approach.

PDF PDF [BibTex]

ei Hill, N., Farquhar, J., Lal, T., Schölkopf, B. Time-Dependent Demixing of Task-Relevant EEG Signals In Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006, pages: 20-21, (Editors: GR Müller-Putz and C Brunner and R Leeb and R Scherer and A Schlögl and S Wriessnegger and G Pfurtscheller), Verlag der Technischen Universität Graz, Graz, Austria, 3rd International Brain-Computer Interface Workshop and Training Course, September 2006 (inproceedings)

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Transductive Gaussian Process Regression with Automatic Model Selection

Le, Q., Smola, A., Gärtner, T., Altun, Y.

In Machine Learning: ECML 2006, pages: 306-317, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning (ECML), September 2006 (inproceedings)

Abstract

n contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at training time. Transductive inference tries to improve the predictive accuracy of learning algorithms by making use of the information contained in these test instances. Although this description of transductive inference applies to predictive learning problems in general, most transductive approaches consider the case of classification only. In this paper we introduce a transductive variant of Gaussian process regression with automatic model selection, based on approximate moment matching between training and test data. Empirical results show the feasibility and competitiveness of this approach.

Web DOI [BibTex]

ei Le, Q., Smola, A., Gärtner, T., Altun, Y. Transductive Gaussian Process Regression with Automatic Model Selection In Machine Learning: ECML 2006, pages: 306-317, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning (ECML), September 2006 (inproceedings)

Web DOI [BibTex]

A Sober Look at Clustering Stability

Ben-David, S., von Luxburg, U., Pal, D.

In COLT 2006, pages: 5-19, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory, September 2006 (inproceedings)

Abstract

Stability is a common tool to verify the validity of sample based algorithms. In clustering it is widely used to tune the parameters of the algorithm, such as the number k of clusters. In spite of the popularity of stability in practical applications, there has been very little theoretical analysis of this notion. In this paper we provide a formal definition of stability and analyze some of its basic properties. Quite surprisingly, the conclusion of our analysis is that for large sample size, stability is fully determined by the behavior of the objective function which the clustering algorithm is aiming to minimize. If the objective function has a unique global minimizer, the algorithm is stable, otherwise it is unstable. In particular we conclude that stability is not a well-suited tool to determine the number of clusters - it is determined by the symmetries of the data which may be unrelated to clustering parameters. We prove our results for center-based clusterings and for spectral clustering, and support our conclusions by many examples in which the behavior of stability is counter-intuitive.

PDF Web DOI [BibTex]

ei Ben-David, S., von Luxburg, U., Pal, D. A Sober Look at Clustering Stability In COLT 2006, pages: 5-19, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory, September 2006 (inproceedings)

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Information Marginalization on Subgraphs

Huang, J., Zhu, T., Rereiner, R., Zhou, D., Schuurmans, D.

In ECML/PKDD 2006, pages: 199-210, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Abstract

Real-world data often involves objects that exhibit multiple relationships; for example, papers and authors exhibit both paper-author interactions and paper-paper citation relationships. A typical learning problem requires one to make inferences about a subclass of objects (e.g. papers), while using the remaining objects and relations to provide relevant information. We present a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. We show that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, we formulate new algorithms for transduction and ranking in relational data, and quantify the performance of new schemes on real world dataachieving good results in many problems.

Web DOI [BibTex]

ei Huang, J., Zhu, T., Rereiner, R., Zhou, D., Schuurmans, D. Information Marginalization on Subgraphs In ECML/PKDD 2006, pages: 199-210, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Web DOI [BibTex]

Bayesian Active Learning for Sensitivity Analysis

Pfingsten, T.

In ECML 2006, pages: 353-364, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning, September 2006 (inproceedings)

Abstract

Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore the behavior of the system, and to compute sensitivity measures as expectations over the input distribution. Monte Carlo methods are a simple approach to estimate these integrals, but they are infeasible when the models are computationally expensive. Using a Gaussian processes prior, expensive simulation runs can be saved. This Bayesian quadrature allows for an active selection of inputs where the simulation promises to be most valuable, and the number of simulation runs can be reduced further. We present an active learning scheme for sensitivity analysis which is rigorously derived from the corresponding Bayesian expected loss. On three fully featured, high dimensional physical models of electro-mechanical sensors, we show that the learning rate in the active learning scheme is significantly better than for passive learning.

PDF Web DOI [BibTex]

ei Pfingsten, T. Bayesian Active Learning for Sensitivity Analysis In ECML 2006, pages: 353-364, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning, September 2006 (inproceedings)

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An Online-Computation Approach to Optimal Finite-Horizon State-Feedback Control of Nonlinear Stochastic Systems

Deisenroth, MP.

Biologische Kybernetik, Universität Karlsruhe (TH), Karlsruhe, Germany, August 2006 (diplomathesis)

PDF [BibTex]

ei Deisenroth, MP. An Online-Computation Approach to Optimal Finite-Horizon State-Feedback Control of Nonlinear Stochastic Systems Biologische Kybernetik, Universität Karlsruhe (TH), Karlsruhe, Germany, August 2006 (diplomathesis)

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From outliers to prototypes: Ordering data

Harmeling, S., Dornhege, G., Tax, D., Meinecke, F., Müller, K.

Neurocomputing, 69(13-15):1608-1618, August 2006 (article)

Abstract

We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show that these easy-to-compute orderings allow us to detect outliers (i.e. very untypical points) with a performance comparable to or better than other often much more sophisticated methods. On the other hand, we show how to use these orderings to detect prototypes (very typical points) which facilitate exploratory data analysis algorithms such as noisy nonlinear dimensionality reduction and clustering. Comprehensive experiments demonstrate the validity of our approach.

PDF PDF DOI [BibTex]

ei Harmeling, S., Dornhege, G., Tax, D., Meinecke, F., Müller, K. From outliers to prototypes: Ordering data Neurocomputing, 69(13-15):1608-1618, August 2006 (article)

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An Online Support Vector Machine for Abnormal Events Detection

Davy, M., Desobry, F., Gretton, A., Doncarli, C.

Signal Processing, 86(8):2009-2025, August 2006 (article)

Abstract

The ability to detect online abnormal events in signals is essential in many real-world Signal Processing applications. Previous algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. Corresponding implementation relies on maximum likelihood or on Bayes estimation theory with generally excellent performance. However, there are numerous cases where a robust and tractable model cannot be obtained, and model-free approaches need to be considered. In this paper, we investigate a machine learning, descriptor-based approach that does not require an explicit descriptors statistical model, based on Support Vector novelty detection. A sequential optimization algorithm is introduced. Theoretical considerations as well as simulations on real signals demonstrate its practical efficiency.

PDF PostScript PDF DOI [BibTex]

ei Davy, M., Desobry, F., Gretton, A., Doncarli, C. An Online Support Vector Machine for Abnormal Events Detection Signal Processing, 86(8):2009-2025, August 2006 (article)

PDF PostScript PDF DOI [BibTex]

Integrating Structured Biological data by Kernel Maximum Mean Discrepancy

Borgwardt, K., Gretton, A., Rasch, M., Kriegel, H., Schölkopf, B., Smola, A.

Bioinformatics, 22(4: ISMB 2006 Conference Proceedings):e49-e57, August 2006 (article)

Abstract

Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology. Results: We study the practical feasibility of an MMD-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors. Conclusions: We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments.

Web DOI [BibTex]

ei Borgwardt, K., Gretton, A., Rasch, M., Kriegel, H., Schölkopf, B., Smola, A. Integrating Structured Biological data by Kernel Maximum Mean Discrepancy Bioinformatics, 22(4: ISMB 2006 Conference Proceedings):e49-e57, August 2006 (article)

Web DOI [BibTex]

Semi-supervised Hyperspectral Image Classification with Graphs

Bandos, T., Zhou, D., Camps-Valls, G.

In IGARSS 2006, pages: 3883-3886, IEEE Computer Society, Los Alamitos, CA, USA, IEEE International Conference on Geoscience and Remote Sensing, August 2006 (inproceedings)

Abstract

This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to exploit the spatial/contextual information in the images through composite kernels. The proposed method produces smoother classifications with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Good accuracy in high dimensional spaces and low number of labeled samples (ill-posed situations) are produced as compared to standard inductive support vector machines.

PDF Web DOI [BibTex]

ei Bandos, T., Zhou, D., Camps-Valls, G. Semi-supervised Hyperspectral Image Classification with Graphs In IGARSS 2006, pages: 3883-3886, IEEE Computer Society, Los Alamitos, CA, USA, IEEE International Conference on Geoscience and Remote Sensing, August 2006 (inproceedings)

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Comment on “Support vector machines with applications” by J. M. Moguerza and A. Muñoz

Bousquet, O., Schölkopf, B.

Statistical Science, 21(3):337-340, August 2006 (article)

DOI [BibTex]

ei Bousquet, O., Schölkopf, B. Comment on “Support vector machines with applications” by J. M. Moguerza and A. Muñoz Statistical Science, 21(3):337-340, August 2006 (article)

DOI [BibTex]

Large Scale Transductive SVMs

Collobert, R., Sinz, F., Weston, J., Bottou, L.

Journal of Machine Learning Research, 7, pages: 1687-1712, August 2006 (article)

Abstract

We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, which traditionally requires solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case. Detailed experiments verify the utility of our approach.

PostScript PDF PDF [BibTex]

ei Collobert, R., Sinz, F., Weston, J., Bottou, L. Large Scale Transductive SVMs Journal of Machine Learning Research, 7, pages: 1687-1712, August 2006 (article)

PostScript PDF PDF [BibTex]

Supervised Probabilistic Principal Component Analysis

Yu, S., Yu, K., Tresp, V., Kriegel, H., Wu, M.

In KDD 2006, pages: 464-473, (Editors: Ungar, L. ), ACM Press, New York, NY, USA, 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2006 (inproceedings)

Abstract

Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When labels of data are available, e.g.,~in a classification or regression task, PCA is however not able to use this information. The problem is more interesting if only part of the input data are labeled, i.e.,~in a semi-supervised setting. In this paper we propose a supervised PCA model called SPPCA and a semi-supervised PCA model called S$^2$PPCA, both of which are extensions of a probabilistic PCA model. The proposed models are able to incorporate the label information into the projection phase, and can naturally handle multiple outputs (i.e.,~in multi-task learning problems). We derive an efficient EM learning algorithm for both models, and also provide theoretical justifications of the model behaviors. SPPCA and S$^2$PPCA are compared with other supervised projection methods on various learning tasks, and show not only promising performance but also good scalability.

PDF Web DOI [BibTex]

ei Yu, S., Yu, K., Tresp, V., Kriegel, H., Wu, M. Supervised Probabilistic Principal Component Analysis In KDD 2006, pages: 464-473, (Editors: Ungar, L. ), ACM Press, New York, NY, USA, 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2006 (inproceedings)

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Finding directional movement representations in motor cortical neural populations using nonlinear manifold learning

WorKim, S., Simeral, J., Jenkins, O., Donoghue, J., Black, M.

World Congress on Medical Physics and Biomedical Engineering 2006, Seoul, Korea, August 2006 (conference)

[BibTex]

ps WorKim, S., Simeral, J., Jenkins, O., Donoghue, J., Black, M. Finding directional movement representations in motor cortical neural populations using nonlinear manifold learning World Congress on Medical Physics and Biomedical Engineering 2006, Seoul, Korea, August 2006 (conference)

[BibTex]

Let It Roll – Emerging Sensorimotor Coordination in a Spherical Robot

Der, R., Martius, G., Hesse, F.

In Proc, Artificial Life X, pages: 192-198, Intl. Society for Artificial Life, MIT Press, August 2006 (inproceedings)

[BibTex]

al Der, R., Martius, G., Hesse, F. Let It Roll – Emerging Sensorimotor Coordination in a Spherical Robot In Proc, Artificial Life X, pages: 192-198, Intl. Society for Artificial Life, MIT Press, August 2006 (inproceedings)

[BibTex]

A non-parametric Bayesian approach to spike sorting

Wood, F., Goldwater, S., Black, M. J.

In International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pages: 1165-1169, New York, NY, August 2006 (inproceedings)

pdf [BibTex]

ps Wood, F., Goldwater, S., Black, M. J. A non-parametric Bayesian approach to spike sorting In International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pages: 1165-1169, New York, NY, August 2006 (inproceedings)

pdf [BibTex]

Building Support Vector Machines with Reduced Classifier Complexity

Keerthi, S., Chapelle, O., DeCoste, D.

Journal of Machine Learning Research, 7, pages: 1493-1515, July 2006 (article)

Abstract

Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size ($dmax$) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as $O(ndmax^2)$ where $n$ is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.

PDF [BibTex]

ei Keerthi, S., Chapelle, O., DeCoste, D. Building Support Vector Machines with Reduced Classifier Complexity Journal of Machine Learning Research, 7, pages: 1493-1515, July 2006 (article)

PDF [BibTex]

Broad-Coverage Sense Disambiguation and Information Extraction with a Supersense Sequence Tagger

Ciaramita, M., Altun, Y.

In pages: 594-602, (Editors: Jurafsky, D. , E. Gaussier), Association for Computational Linguistics, Stroudsburg, PA, USA, 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP), July 2006 (inproceedings)

Abstract

In this paper we approach word sense disambiguation and information extraction as a unified tagging problem. The task consists of annotating text with the tagset defined by the 41 Wordnet supersense classes for nouns and verbs. Since the tagset is directly related to Wordnet synsets, the tagger returns partial word sense disambiguation. Furthermore, since the noun tags include the standard named entity detection classes – person, location, organization, time, etc. – the tagger, as a by-product, returns extended named entity information. We cast the problem of supersense tagging as a sequential labeling task and investigate it empirically with a discriminatively-trained Hidden Markov Model. Experimental evaluation on the main sense-annotated datasets available, i.e., Semcor and Senseval, shows considerable improvements over the best known “first-sense” baseline.

Web [BibTex]

ei Ciaramita, M., Altun, Y. Broad-Coverage Sense Disambiguation and Information Extraction with a Supersense Sequence Tagger In pages: 594-602, (Editors: Jurafsky, D. , E. Gaussier), Association for Computational Linguistics, Stroudsburg, PA, USA, 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP), July 2006 (inproceedings)

Web [BibTex]

ARTS: Accurate Recognition of Transcription Starts in Human

Sonnenburg, S., Zien, A., Rätsch, G.

Bioinformatics, 22(14):e472-e480, July 2006 (article)

Abstract

Motivation: One of the most important features of genomic DNA are the protein-coding genes. While it is of great value to identify those genes and the encoded proteins, it is also crucial to understand how their transcription is regulated. To this end one has to identify the corresponding promoters and the contained transcription factor binding sites. TSS finders can be used to locate potential promoters. They may also be used in combination with other signal and content detectors to resolve entire gene structures. Results: We have developed a novel kernel based method - called ARTS - that accurately recognizes transcription start sites in human. The application of otherwise too computationally expensive Support Vector Machines was made possible due to the use of efficient training and evaluation techniques using suffix tries. In a carefully designed experimental study, we compare our TSS finder to state-of-the-art methods from the literature: McPromoter, Eponine and FirstEF. For given false positive rates within a reasonable range, we consistently achieve considerably higher true positive rates. For instance, ARTS finds about 24% true positives at a false positive rate of 1/1000, where the other methods find less than half (10.5%). Availability: Datasets, model selection results, whole genome predictions, and additional experimental results are available at http://www.fml.tuebingen.mpg.de/raetsch/projects/arts

Web DOI [BibTex]

ei Sonnenburg, S., Zien, A., Rätsch, G. ARTS: Accurate Recognition of Transcription Starts in Human Bioinformatics, 22(14):e472-e480, July 2006 (article)

Web DOI [BibTex]

Large Scale Multiple Kernel Learning

Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.

Journal of Machine Learning Research, 7, pages: 1531-1565, July 2006 (article)

Abstract

While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimental results show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined, and helps for automatic model selection, improving the interpretability of the learning result. In a second part we discuss general speed up mechanism for SVMs, especially when used with sparse feature maps as appear for string kernels, allowing us to train a string kernel SVM on a 10 million real-world splice data set from computational biology. We integrated multiple kernel learning in our machine learning toolbox SHOGUN for which the source code is publicly available at http://www.fml.tuebingen.mpg.de/raetsch/projects/shogun.

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ei Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B. Large Scale Multiple Kernel Learning Journal of Machine Learning Research, 7, pages: 1531-1565, July 2006 (article)

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Predicting 3D people from 2D pictures

(Best Paper)

Sigal, L., Black, M. J.

In Proc. IV Conf. on Articulated Motion and DeformableObjects (AMDO), LNCS 4069, pages: 185-195, July 2006 (inproceedings)

Abstract

We propose a hierarchical process for inferring the 3D pose of a person from monocular images. First we infer a learned view-based 2D body model from a single image using non-parametric belief propagation. This approach integrates information from bottom-up body-part proposal processes and deals with self-occlusion to compute distributions over limb poses. Then, we exploit a learned Mixture of Experts model to infer a distribution of 3D poses conditioned on 2D poses. This approach is more general than recent work on inferring 3D pose directly from silhouettes since the 2D body model provides a richer representation that includes the 2D joint angles and the poses of limbs that may be unobserved in the silhouette. We demonstrate the method in a laboratory setting where we evaluate the accuracy of the 3D poses against ground truth data. We also estimate 3D body pose in a monocular image sequence. The resulting 3D estimates are sufficiently accurate to serve as proposals for the Bayesian inference of 3D human motion over time

pdf pdf from publisher Video [BibTex]

ps Sigal, L., Black, M. J. Predicting 3D people from 2D pictures In Proc. IV Conf. on Articulated Motion and DeformableObjects (AMDO), LNCS 4069, pages: 185-195, July 2006 (inproceedings)

pdf pdf from publisher Video [BibTex]

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