Publications | Max Planck Institute for Intelligent Systems

160 results (View BibTeX file of all listed publications)

2009

Beamforming in Noninvasive Brain-Computer Interfaces

Grosse-Wentrup, M., Liefhold, C., Gramann, K., Buss, M.

IEEE Transactions on Biomedical Engineering, 56(4):1209-1219, April 2009 (article)

Abstract

Spatial filtering (SF) constitutes an integral part of building EEG-based braincomputer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject‘s intention, which renders these algorithms susceptible to overfitting on artifactual EEG components. In this study, beamforming is employed to construct spatial filters that extract EEG sources originating within predefined regions of interest within the brain. In this way, neurophysiological knowledge on which brain regions are relevant for a certain experimental paradigm can be utilized to construct unsupervised spatial filters that are robust against artifactual EEG components. Beamforming is experimentally compared with CSP and Laplacian spatial filtering (LP) in a two-class motor-imagery paradigm. It is demonstrated that beamforming outperforms CSP and LP on noisy datasets, while CSP and beamforming perform almost equally well on datasets with few artifactual trials. It is concluded that beamforming constitutes an alternative method for SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.

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2009

ei Grosse-Wentrup, M., Liefhold, C., Gramann, K., Buss, M. Beamforming in Noninvasive Brain-Computer Interfaces IEEE Transactions on Biomedical Engineering, 56(4):1209-1219, April 2009 (article)

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Constructing Sparse Kernel Machines Using Attractors

Lee, D., Jung, K., Lee, J.

IEEE Transactions on Neural Networks, 20(4):721-729, April 2009 (article)

Abstract

In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed. The simulation results show that the constructed sparse kernel machine improves the efficiency of testing phase while maintaining comparable test error.

Web DOI [BibTex]

ei Lee, D., Jung, K., Lee, J. Constructing Sparse Kernel Machines Using Attractors IEEE Transactions on Neural Networks, 20(4):721-729, April 2009 (article)

Web DOI [BibTex]

Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters

Maier, M., Hein, M., von Luxburg, U.

Theoretical Computer Science, 410(19):1749-1764, April 2009 (article)

Abstract

We study clustering algorithms based on neighborhood graphs on a random sample of data points. The question we ask is how such a graph should be constructed in order to obtain optimal clustering results. Which type of neighborhood graph should one choose, mutual k-nearest-neighbor or symmetric k-nearest-neighbor? What is the optimal parameter k? In our setting, clusters are defined as connected components of the t-level set of the underlying probability distribution. Clusters are said to be identified in the neighborhood graph if connected components in the graph correspond to the true underlying clusters. Using techniques from random geometric graph theory, we prove bounds on the probability that clusters are identified successfully, both in a noise-free and in a noisy setting. Those bounds lead to several conclusions. First, k has to be chosen surprisingly high (rather of the order n than of the order logn) to maximize the probability of cluster identification. Secondly, the major difference between the mutual and the symmetric k-nearest-neighbor graph occurs when one attempts to detect the most significant cluster only.

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ei Maier, M., Hein, M., von Luxburg, U. Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters Theoretical Computer Science, 410(19):1749-1764, April 2009 (article)

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Overlap and refractory effects in a Brain-Computer Interface speller based on the visual P300 Event-Related Potential

Martens, S., Hill, N., Farquhar, J., Schölkopf, B.

Journal of Neural Engineering, 6(2):1-9, April 2009 (article)

Abstract

We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.

PDF DOI [BibTex]

ei Martens, S., Hill, N., Farquhar, J., Schölkopf, B. Overlap and refractory effects in a Brain-Computer Interface speller based on the visual P300 Event-Related Potential Journal of Neural Engineering, 6(2):1-9, April 2009 (article)

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Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions

Bubeck, S., von Luxburg, U.

Journal of Machine Learning Research, 10, pages: 657-698, March 2009 (article)

Abstract

Clustering is often formulated as a discrete optimization problem. The objective is to find, among all partitions of the data set, the best one according to some quality measure. However, in the statistical setting where we assume that the finite data set has been sampled from some underlying space, the goal is not to find the best partition of the given sample, but to approximate the true partition of the underlying space. We argue that the discrete optimization approach usually does not achieve this goal, and instead can lead to inconsistency. We construct examples which provably have this behavior. As in the case of supervised learning, the cure is to restrict the size of the function classes under consideration. For appropriate small function classes we can prove very general consistency theorems for clustering optimization schemes. As one particular algorithm for clustering with a restricted function space we introduce nearest neighbor clustering. Similar to the k-nearest neighbor classifier in supervised learning, this algorithm can be seen as a general baseline algorithm to minimize arbitrary clustering objective functions. We prove that it is statistically consistent for all commonly used clustering objective functions.

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ei Bubeck, S., von Luxburg, U. Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions Journal of Machine Learning Research, 10, pages: 657-698, March 2009 (article)

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Model selection, large deviations and consistency of data-driven tests

Langovoy, M.

(2009-007), EURANDOM, Technische Universiteit Eindhoven, March 2009 (techreport)

Abstract

We consider three general classes of data-driven statistical tests. Neyman's smooth tests, data-driven score tests and data-driven score tests for statistical inverse problems serve as important special examples for the classes of tests under consideration. Our tests are additionally incorporated with model selection rules. The rules are based on the penalization idea. Most of the optimal penalties, derived in statistical literature, can be used in our tests. We prove general consistency theorems for the tests from those classes. Our proofs make use of large deviations inequalities for deterministic and random quadratic forms. The paper shows that the tests can be applied for simple and composite parametric, semi- and nonparametric hypotheses. Applications to testing in statistical inverse problems and statistics for stochastic processes are also presented..

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ei Langovoy, M. Model selection, large deviations and consistency of data-driven tests (2009-007), EURANDOM, Technische Universiteit Eindhoven, March 2009 (techreport)

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Protein Functional Class Prediction With a Combined Graph

Shin, H., Tsuda, K., Schölkopf, B.

Expert Systems with Applications, 36(2):3284-3292, March 2009 (article)

Abstract

In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as proteinprotein interactions, genetic interactions, or co-participation in a protein complex, etc. Relying on similarities between nodes, each graph can be used independently for prediction of protein function. However, since different graphs contain partly independent and partly complementary information about the problem at hand, one can enhance the total information extracted by combining all graphs. In this paper, we propose a method for integrating multiple graphs within a framework of semi-supervised learning. The method alternates between minimizing the objective function with respect to network output and with respect to combining weights. We apply the method to the task of protein functional class prediction in yeast. The proposed method performs significantly better than the same algorithm trained on any singl e graph.

Web DOI [BibTex]

ei Shin, H., Tsuda, K., Schölkopf, B. Protein Functional Class Prediction With a Combined Graph Expert Systems with Applications, 36(2):3284-3292, March 2009 (article)

Web DOI [BibTex]

Gaussian Process Dynamic Programming

Deisenroth, M., Rasmussen, C., Peters, J.

Neurocomputing, 72(7-9):1508-1524, March 2009 (article)

Abstract

Reinforcement learning (RL) and optimal control of systems with contin- uous states and actions require approximation techniques in most interesting cases. In this article, we introduce Gaussian process dynamic programming (GPDP), an approximate value-function based RL algorithm. We consider both a classic optimal control problem, where problem-specific prior knowl- edge is available, and a classic RL problem, where only very general priors can be used. For the classic optimal control problem, GPDP models the unknown value functions with Gaussian processes and generalizes dynamic programming to continuous-valued states and actions. For the RL problem, GPDP starts from a given initial state and explores the state space using Bayesian active learning. To design a fast learner, available data has to be used efficiently. Hence, we propose to learn probabilistic models of the a priori unknown transition dynamics and the value functions on the fly. In both cases, we successfully apply the resulting continuous-valued controllers to the under-actuated pendulum swing up and analyze the performances of the suggested algorithms. It turns out that GPDP uses data very efficiently and can be applied to problems, where classic dynamic programming would be cumbersome.

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ei Deisenroth, M., Rasmussen, C., Peters, J. Gaussian Process Dynamic Programming Neurocomputing, 72(7-9):1508-1524, March 2009 (article)

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An algebraic characterization of the optimum of regularized kernel methods

Dinuzzo, F., De Nicolao, G.

Machine Learning, 74(3):315-345, March 2009 (article)

Abstract

The representer theorem for kernel methods states that the solution of the associated variational problem can be expressed as the linear combination of a finite number of kernel functions. However, for non-smooth loss functions, the analytic characterization of the coefficients poses nontrivial problems. Standard approaches resort to constrained optimization reformulations which, in general, lack a closed-form solution. Herein, by a proper change of variable, it is shown that, for any convex loss function, the coefficients satisfy a system of algebraic equations in a fixed-point form, which may be directly obtained from the primal formulation. The algebraic characterization is specialized to regression and classification methods and the fixed-point equations are explicitly characterized for many loss functions of practical interest. The consequences of the main result are then investigated along two directions. First, the existence of an unconstrained smooth reformulation of the original non-smooth problem is proven. Second, in the context of SURE (Stein’s Unbiased Risk Estimation), a general formula for the degrees of freedom of kernel regression methods is derived.

PDF DOI [BibTex]

ei Dinuzzo, F., De Nicolao, G. An algebraic characterization of the optimum of regularized kernel methods Machine Learning, 74(3):315-345, March 2009 (article)

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Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques

Hofmann, M., Pichler, B., Schölkopf, B., Beyer, T.

European Journal of Nuclear Medicine and Molecular Imaging, 36(Supplement 1):93-104, March 2009 (article)

Abstract

Introduction Positron emission tomography (PET) is a fully quantitative technology for imaging metabolic pathways and dynamic processes in vivo. Attenuation correction of raw PET data is a prerequisite for quantification and is typically based on separate transmission measurements. In PET/CT attenuation correction, however, is performed routinely based on the available CT transmission data. Objective Recently, combined PET/magnetic resonance (MR) has been proposed as a viable alternative to PET/CT. Current concepts of PET/MRI do not include CT-like transmission sources and, therefore, alternative methods of PET attenuation correction must be found. This article reviews existing approaches to MR-based attenuation correction (MR-AC). Most groups have proposed MR-AC algorithms for brain PET studies and more recently also for torso PET/MR imaging. Most MR-AC strategies require the use of complementary MR and transmission images, or morphology templates generated from transmission images. We review and discuss these algorithms and point out challenges for using MR-AC in clinical routine. Discussion MR-AC is work-in-progress with potentially promising results from a template-based approach applicable to both brain and torso imaging. While efforts are ongoing in making clinically viable MR-AC fully automatic, further studies are required to realize the potential benefits of MR-based motion compensation and partial volume correction of the PET data.

PDF DOI [BibTex]

ei Hofmann, M., Pichler, B., Schölkopf, B., Beyer, T. Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques European Journal of Nuclear Medicine and Molecular Imaging, 36(Supplement 1):93-104, March 2009 (article)

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Generating Spike Trains with Specified Correlation Coefficients

Macke, J., Berens, P., Ecker, A., Tolias, A., Bethge, M.

Neural Computation, 21(2):397-423, February 2009 (article)

Abstract

Spike trains recorded from populations of neurons can exhibit substantial pairwise correlations between neurons and rich temporal structure. Thus, for the realistic simulation and analysis of neural systems, it is essential to have efficient methods for generating artificial spike trains with specified correlation structure. Here we show how correlated binary spike trains can be simulated by means of a latent multivariate gaussian model. Sampling from the model is computationally very efficient and, in particular, feasible even for large populations of neurons. The entropy of the model is close to the theoretical maximum for a wide range of parameters. In addition, this framework naturally extends to correlations over time and offers an elegant way to model correlated neural spike counts with arbitrary marginal distributions.

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ei Macke, J., Berens, P., Ecker, A., Tolias, A., Bethge, M. Generating Spike Trains with Specified Correlation Coefficients Neural Computation, 21(2):397-423, February 2009 (article)

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Automatic detection of preclinical neurodegeneration: Presymptomatic Huntington disease

Klöppel, S., Chu, C., Tan, G., Draganski, B., Johnson, H., Paulsen, J., Kienzle, W., Tabrizi, S., Ashburner, J., Frackowiak, R.

Neurology, 72(5):426-431, February 2009 (article)

Abstract

Background: Treatment of neurodegenerative diseases is likely to be most beneficial in the very early, possibly preclinical stages of degeneration. We explored the usefulness of fully automatic structural MRI classification methods for detecting subtle degenerative change. The availability of a definitive genetic test for Huntington disease (HD) provides an excellent metric for judging the performance of such methods in gene mutation carriers who are free of symptoms. Methods: Using the gray matter segment of MRI scans, this study explored the usefulness of a multivariate support vector machine to automatically identify presymptomatic HD gene mutation carriers (PSCs) in the absence of any a priori information. A multicenter data set of 96 PSCs and 95 age- and sex-matched controls was studied. The PSC group was subclassified into three groups based on time from predicted clinical onset, an estimate that is a function of DNA mutation size and age. Results: Subjects with at least a 33% chance of developing unequivocal signs of HD in 5 years were correctly assigned to the PSC group 69% of the time. Accuracy improved to 83% when regions affected by the disease were selected a priori for analysis. Performance was at chance when the probability of developing symptoms in 5 years was less than 10%. Conclusions: Presymptomatic Huntington disease gene mutation carriers close to estimated diagnostic onset were successfully separated from controls on the basis of single anatomic scans, without additional a priori information. Prior information is required to allow separation when degenerative changes are either subtle or variable.

Web [BibTex]

ei Klöppel, S., Chu, C., Tan, G., Draganski, B., Johnson, H., Paulsen, J., Kienzle, W., Tabrizi, S., Ashburner, J., Frackowiak, R. Automatic detection of preclinical neurodegeneration: Presymptomatic Huntington disease Neurology, 72(5):426-431, February 2009 (article)

Web [BibTex]

Enumeration of condition-dependent dense modules in protein interaction networks

Georgii, E., Dietmann, S., Uno, T., Pagel, P., Tsuda, K.

Bioinformatics, 25(7):933-940, February 2009 (article)

Abstract

Motivation: Modern systems biology aims at understanding how the different molecular components of a biological cell interact. Often, cellular functions are performed by complexes consisting of many different proteins. The composition of these complexes may change according to the cellular environment, and one protein may be involved in several different processes. The automatic discovery of functional complexes from protein interaction data is challenging. While previous approaches use approximations to extract dense modules, our approach exactly solves the problem of dense module enumeration. Furthermore, constraints from additional information sources such as gene expression and phenotype data can be integrated, so we can systematically mine for dense modules with interesting profiles. Results: Given a weighted protein interaction network, our method discovers all protein sets that satisfy a user-defined minimum density threshold. We employ a reverse search strategy, which allows us to exploit the density criterion in an efficient way. Our experiments show that the novel approach is feasible and produces biologically meaningful results. In comparative validation studies using yeast data, the method achieved the best overall prediction performance with respect to confirmed complexes. Moreover, by enhancing the yeast network with phenotypic and phylogenetic profiles and the human network with tissue-specific expression data, we identified condition-dependent complex variants.

Web DOI [BibTex]

ei Georgii, E., Dietmann, S., Uno, T., Pagel, P., Tsuda, K. Enumeration of condition-dependent dense modules in protein interaction networks Bioinformatics, 25(7):933-940, February 2009 (article)

Web DOI [BibTex]

Prototype Classification: Insights from Machine Learning

Graf, A., Bousquet, O., Rätsch, G., Schölkopf, B.

Neural Computation, 21(1):272-300, January 2009 (article)

Abstract

We shed light on the discrimination between patterns belonging to two different classes by casting this decoding problem into a generalized prototype framework. The discrimination process is then separated into two stages: a projection stage that reduces the dimensionality of the data by projecting it on a line and a threshold stage where the distributions of the projected patterns of both classes are separated. For this, we extend the popular mean-of-class prototype classification using algorithms from machine learning that satisfy a set of invariance properties. We report a simple yet general approach to express different types of linear classification algorithms in an identical and easy-to-visualize formal framework using generalized prototypes where these prototypes are used to express the normal vector and offset of the hyperplane. We investigate nonmargin classifiers such as the classical prototype classifier, the Fisher classifier, and the relevance vector machine. We then study hard and soft margin cl assifiers such as the support vector machine and a boosted version of the prototype classifier. Subsequently, we relate mean-of-class prototype classification to other classification algorithms by showing that the prototype classifier is a limit of any soft margin classifier and that boosting a prototype classifier yields the support vector machine. While giving novel insights into classification per se by presenting a common and unified formalism, our generalized prototype framework also provides an efficient visualization and a principled comparison of machine learning classification.

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ei Graf, A., Bousquet, O., Rätsch, G., Schölkopf, B. Prototype Classification: Insights from Machine Learning Neural Computation, 21(1):272-300, January 2009 (article)

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Automatic classification of brain resting states using fMRI temporal signals

Soldati, N., Robinson, S., Persello, C., Jovicich, J., Bruzzone, L.

Electronics Letters, 45(1):19-21, January 2009 (article)

Abstract

A novel technique is presented for the automatic discrimination between networks of dasiaresting statesdasia of the human brain and physiological fluctuations in functional magnetic resonance imaging (fMRI). The method is based on features identified via a statistical approach to group independent component analysis time courses, which may be extracted from fMRI data. This technique is entirely automatic and, unlike other approaches, uses temporal rather than spatial information. The method achieves 83% accuracy in the identification of resting state networks.

Web DOI [BibTex]

ei Soldati, N., Robinson, S., Persello, C., Jovicich, J., Bruzzone, L. Automatic classification of brain resting states using fMRI temporal signals Electronics Letters, 45(1):19-21, January 2009 (article)

Web DOI [BibTex]

The DICS repository: module-assisted analysis of disease-related gene lists

Dietmann, S., Georgii, E., Antonov, A., Tsuda, K., Mewes, H.

Bioinformatics, 25(6):830-831, January 2009 (article)

Abstract

The DICS database is a dynamic web repository of computationally predicted functional modules from the human proteinprotein interaction network. It provides references to the CORUM, DrugBank, KEGG and Reactome pathway databases. DICS can be accessed for retrieving sets of overlapping modules and protein complexes that are significantly enriched in a gene list, thereby providing valuable information about the functional context.

Web DOI [BibTex]

ei Dietmann, S., Georgii, E., Antonov, A., Tsuda, K., Mewes, H. The DICS repository: module-assisted analysis of disease-related gene lists Bioinformatics, 25(6):830-831, January 2009 (article)

Web DOI [BibTex]

Pre−processed feature ranking for a support vector machine

Weston, J., Elisseeff, A., Schölkopf, B., Pérez-Cruz, F., Guyon, I.

United States Patent, No. 7475048, January 2009 (patent)

[BibTex]

ei Weston, J., Elisseeff, A., Schölkopf, B., Pérez-Cruz, F., Guyon, I. Pre−processed feature ranking for a support vector machine United States Patent, No. 7475048, January 2009 (patent)

[BibTex]

mGene: accurate SVM-based gene finding with an application to nematode genomes

Schweikert, G., Zien, A., Zeller, G., Behr, J., Dieterich, C., Ong, C., Philips, P., De Bona, F., Hartmann, L., Bohlen, A., Krüger, N., Sonnenburg, S., Rätsch, G.

Genome Research, 19(11):2133-43, 2009 (article)

Abstract

We present a highly accurate gene-prediction system for eukaryotic genomes, called mGene. It combines in an unprecedented manner the flexibility of generalized hidden Markov models (gHMMs) with the predictive power of modern machine learning methods, such as Support Vector Machines (SVMs). Its excellent performance was proved in an objective competition based on the genome of the nematode Caenorhabditis elegans. Considering the average of sensitivity and specificity, the developmental version of mGene exhibited the best prediction performance on nucleotide, exon, and transcript level for ab initio and multiple-genome gene-prediction tasks. The fully developed version shows superior performance in 10 out of 12 evaluation criteria compared with the other participating gene finders, including Fgenesh++ and Augustus. An in-depth analysis of mGene's genome-wide predictions revealed that approximately 2200 predicted genes were not contained in the current genome annotation. Testing a subset of 57 of these genes by RT-PCR and sequencing, we confirmed expression for 24 (42%) of them. mGene missed 300 annotated genes, out of which 205 were unconfirmed. RT-PCR testing of 24 of these genes resulted in a success rate of merely 8%. These findings suggest that even the gene catalog of a well-studied organism such as C. elegans can be substantially improved by mGene's predictions. We also provide gene predictions for the four nematodes C. briggsae, C. brenneri, C. japonica, and C. remanei. Comparing the resulting proteomes among these organisms and to the known protein universe, we identified many species-specific gene inventions. In a quality assessment of several available annotations for these genomes, we find that mGene's predictions are most accurate.

DOI [BibTex]

ei Schweikert, G., Zien, A., Zeller, G., Behr, J., Dieterich, C., Ong, C., Philips, P., De Bona, F., Hartmann, L., Bohlen, A., Krüger, N., Sonnenburg, S., Rätsch, G. mGene: accurate SVM-based gene finding with an application to nematode genomes Genome Research, 19(11):2133-43, 2009 (article)

DOI [BibTex]

Structure and activity of the N-terminal substrate recognition domains in proteasomal ATPases

Djuranovic, S., Hartmann, MD., Habeck, M., Ursinus, A., Zwickl, P., Martin, J., Lupas, AN., Zeth, K.

Molecular Cell, 34(5):580-590, 2009 (article)

Abstract

The proteasome forms the core of the protein quality control system in archaea and eukaryotes and also occurs in one bacterial lineage, the Actinobacteria. Access to its proteolytic compartment is controlled by AAA ATPases, whose N-terminal domains (N domains) are thought to mediate substrate recognition. The N domains of an archaeal proteasomal ATPase, Archaeoglobus fulgidus PAN, and of its actinobacterial homolog, Rhodococcus erythropolis ARC, form hexameric rings, whose subunits consist of an N-terminal coiled coil and a C-terminal OB domain. In ARC-N, the OB domains are duplicated and form separate rings. PAN-N and ARC-N can act as chaperones, preventing the aggregation of heterologous proteins in vitro, and this activity is preserved in various chimeras, even when these include coiled coils and OB domains from unrelated proteins. The structures suggest a molecular mechanism for substrate processing based on concerted radial motions of the coiled coils relative to the OB rings.

DOI [BibTex]

ei Djuranovic, S., Hartmann, MD., Habeck, M., Ursinus, A., Zwickl, P., Martin, J., Lupas, AN., Zeth, K. Structure and activity of the N-terminal substrate recognition domains in proteasomal ATPases Molecular Cell, 34(5):580-590, 2009 (article)

DOI [BibTex]

Discussion of: Brownian Distance Covariance

Gretton, A., Fukumizu, K., Sriperumbudur, B.

The Annals of Applied Statistics, 3(4):1285-1294, 2009 (article)

[BibTex]

ei Gretton, A., Fukumizu, K., Sriperumbudur, B. Discussion of: Brownian Distance Covariance The Annals of Applied Statistics, 3(4):1285-1294, 2009 (article)

[BibTex]

Efficient factor GARCH models and factor-DCC models

Zhang, K., Chan, L.

Quantitative Finance, 9(1):71-91, 2009 (article)

Abstract

We report that, in the estimation of univariate GARCH or multivariate generalized orthogonal GARCH (GO-GARCH) models, maximizing the likelihood is equivalent to making the standardized residuals as independent as possible. Based on this, we propose three factor GARCH models in the framework of GO-GARCH: independent-factor GARCH exploits factors that are statistically as independent as possible; factors in best-factor GARCH have the largest autocorrelation in their squared values such that their volatilities could be forecast well by univariate GARCH; and factors in conditional-decorrelation GARCH are conditionally as uncorrelated as possible. A convenient two-step method for estimating these models is introduced. Since the extracted factors may still have weak conditional correlations, we further propose factor-DCC models as an extension to the above factor GARCH models with dynamic conditional correlation (DCC) modelling the remaining conditional correlations between factors. Experimental results for the Hong Kong stock market show that conditional-decorrelation GARCH and independent-factor GARCH have better generalization performance than the original GO-GARCH, and that conditional-decorrelation GARCH (among factor GARCH models) and its extension with DCC embedded (among factor-DCC models) behave best.

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ei Zhang, K., Chan, L. Efficient factor GARCH models and factor-DCC models Quantitative Finance, 9(1):71-91, 2009 (article)

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Non-linear System Identification: Visual Saliency Inferred from Eye-Movement Data

Wichmann, F., Kienzle, W., Schölkopf, B., Franz, M.

Journal of Vision, 9(8):article 32, 2009 (article)

Abstract

For simple visual patterns under the experimenter's control we impose which information, or features, an observer can use to solve a given perceptual task. For natural vision tasks, however, there are typically a multitude of potential features in a given visual scene which the visual system may be exploiting when analyzing it: edges, corners, contours, etc. Here we describe a novel non-linear system identification technique based on modern machine learning methods that allows the critical features an observer uses to be inferred directly from the observer's data. The method neither requires stimuli to be embedded in noise nor is it limited to linear perceptive fields (classification images). We demonstrate our technique by deriving the critical image features observers fixate in natural scenes (bottom-up visual saliency). Unlike previous studies where the relevant structure is determined manuallyâ€”e.g. by selecting Gabors as visual filtersâ€”we do not make any assumptions in this regard, but numerically infer number and properties them from the eye-movement data. We show that center-surround patterns emerge as the optimal solution for predicting saccade targets from local image structure. The resulting model, a one-layer feed-forward network with contrast gain-control, is surprisingly simple compared to previously suggested saliency models. Nevertheless, our model is equally predictive. Furthermore, our findings are consistent with neurophysiological hardware in the superior colliculus. Bottom-up visual saliency may thus not be computed cortically as has been thought previously.

Web DOI [BibTex]

ei Wichmann, F., Kienzle, W., Schölkopf, B., Franz, M. Non-linear System Identification: Visual Saliency Inferred from Eye-Movement Data Journal of Vision, 9(8):article 32, 2009 (article)

Web DOI [BibTex]

mGene.web: a web service for accurate computational gene finding

Schweikert, G., Behr, J., Zien, A., Zeller, G., Ong, C., Sonnenburg, S., Rätsch, G.

Nucleic Acids Research, 37, pages: W312-6, 2009 (article)

Abstract

We describe mGene.web, a web service for the genome-wide prediction of protein coding genes from eukaryotic DNA sequences. It offers pre-trained models for the recognition of gene structures including untranslated regions in an increasing number of organisms. With mGene.web, users have the additional possibility to train the system with their own data for other organisms on the push of a button, a functionality that will greatly accelerate the annotation of newly sequenced genomes. The system is built in a highly modular way, such that individual components of the framework, like the promoter prediction tool or the splice site predictor, can be used autonomously. The underlying gene finding system mGene is based on discriminative machine learning techniques and its high accuracy has been demonstrated in an international competition on nematode genomes. mGene.web is available at http://www.mgene.org/web, it is free of charge and can be used for eukaryotic genomes of small to moderate size (several hundred Mbp).

DOI [BibTex]

ei Schweikert, G., Behr, J., Zien, A., Zeller, G., Ong, C., Sonnenburg, S., Rätsch, G. mGene.web: a web service for accurate computational gene finding Nucleic Acids Research, 37, pages: W312-6, 2009 (article)

DOI [BibTex]

2005

Kernel Methods for Measuring Independence

Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.

Journal of Machine Learning Research, 6, pages: 2075-2129, December 2005 (article)

Abstract

We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the covariance between functions of the random variables in reproducing kernel Hilbert spaces (RKHSs). We prove that when the RKHSs are universal, both functionals are zero if and only if the random variables are pairwise independent. We also show that the kernel mutual information is an upper bound near independence on the Parzen window estimate of the mutual information. Analogous results apply for two correlation-based dependence functionals introduced earlier: we show the kernel canonical correlation and the kernel generalised variance to be independence measures for universal kernels, and prove the latter to be an upper bound on the mutual information near independence. The performance of the kernel dependence functionals in measuring independence is verified in the context of independent component analysis.

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2005

ei Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B. Kernel Methods for Measuring Independence Journal of Machine Learning Research, 6, pages: 2075-2129, December 2005 (article)

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A Unifying View of Sparse Approximate Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1935-1959, December 2005 (article)

Abstract

We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

PDF [BibTex]

ei Quinonero Candela, J., Rasmussen, C. A Unifying View of Sparse Approximate Gaussian Process Regression Journal of Machine Learning Research, 6, pages: 1935-1959, December 2005 (article)

PDF [BibTex]

Method and device for detection of splice form and alternative splice forms in DNA or RNA sequences

Rätsch, G., Sonnenburg, S., Müller, K., Schölkopf, B.

European Patent Application, International No PCT/EP2005/005783, December 2005 (patent)

[BibTex]

ei Rätsch, G., Sonnenburg, S., Müller, K., Schölkopf, B. Method and device for detection of splice form and alternative splice forms in DNA or RNA sequences European Patent Application, International No PCT/EP2005/005783, December 2005 (patent)

[BibTex]

Popper, Falsification and the VC-dimension

Corfield, D., Schölkopf, B., Vapnik, V.

(145), Max Planck Institute for Biological Cybernetics, November 2005 (techreport)

PDF [BibTex]

ei Corfield, D., Schölkopf, B., Vapnik, V. Popper, Falsification and the VC-dimension (145), Max Planck Institute for Biological Cybernetics, November 2005 (techreport)

PDF [BibTex]

Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O., Schölkopf, B.

Journal of Computer and System Sciences, 71(3):333-359, October 2005 (article)

Abstract

In order to apply the maximum margin method in arbitrary metric spaces, we suggest to embed the metric space into a Banach or Hilbert space and to perform linear classification in this space. We propose several embeddings and recall that an isometric embedding in a Banach space is always possible while an isometric embedding in a Hilbert space is only possible for certain metric spaces. As a result, we obtain a general maximum margin classification algorithm for arbitrary metric spaces (whose solution is approximated by an algorithm of Graepel. Interestingly enough, the embedding approach, when applied to a metric which can be embedded into a Hilbert space, yields the SVM algorithm, which emphasizes the fact that its solution depends on the metric and not on the kernel. Furthermore we give upper bounds of the capacity of the function classes corresponding to both embeddings in terms of Rademacher averages. Finally we compare the capacities of these function classes directly.

PDF PDF DOI [BibTex]

ei Hein, M., Bousquet, O., Schölkopf, B. Maximal Margin Classification for Metric Spaces Journal of Computer and System Sciences, 71(3):333-359, October 2005 (article)

PDF PDF DOI [BibTex]

Selective integration of multiple biological data for supervised network inference

Kato, T., Tsuda, K., Asai, K.

Bioinformatics, 21(10):2488 , October 2005 (article)

PDF [BibTex]

ei Kato, T., Tsuda, K., Asai, K. Selective integration of multiple biological data for supervised network inference Bioinformatics, 21(10):2488 , October 2005 (article)

PDF [BibTex]

Assessing Approximate Inference for Binary Gaussian Process Classification

Kuss, M., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1679 , October 2005 (article)

Abstract

Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace‘s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling. We explain theoretically and corroborate empirically the advantages of Expectation Propagation compared to Laplace‘s method.

PDF PDF [BibTex]

ei Kuss, M., Rasmussen, C. Assessing Approximate Inference for Binary Gaussian Process Classification Journal of Machine Learning Research, 6, pages: 1679 , October 2005 (article)

PDF PDF [BibTex]

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Banerjee, A., Dhillon, I., Ghosh, J., Sra, S.

Journal of Machine Learning Research, 6, pages: 1345-1382, September 2005 (article)

Abstract

Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression data based on a mixture of vMF distributions show that the ability to estimate the concentration parameter for each vMF component, which is not present in existing approaches, yields superior results, especially for difficult clustering tasks in high-dimensional spaces.

PDF [BibTex]

ei Banerjee, A., Dhillon, I., Ghosh, J., Sra, S. Clustering on the Unit Hypersphere using von Mises-Fisher Distributions Journal of Machine Learning Research, 6, pages: 1345-1382, September 2005 (article)

PDF [BibTex]

Support Vector Machines for 3D Shape Processing

Steinke, F., Schölkopf, B., Blanz, V.

Computer Graphics Forum, 24(3, EUROGRAPHICS 2005):285-294, September 2005 (article)

Abstract

We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the art in machine learning. It is straightforward to implement and computationally competitive; its parameters can be automatically set using standard machine learning methods. The surface approximation is based on a modified Support Vector regression. We present applications to 3D head reconstruction, including automatic removal of outliers and hole filling. In a second step, we build on our SV representation to compute dense 3D deformation fields between two objects. The fields are computed using a generalized SVMachine enforcing correspondence between the previously learned implicit SV object representations, as well as correspondences between feature points if such points are available. We apply the method to the morphing of 3D heads and other objects.

PDF [BibTex]

ei Steinke, F., Schölkopf, B., Blanz, V. Support Vector Machines for 3D Shape Processing Computer Graphics Forum, 24(3, EUROGRAPHICS 2005):285-294, September 2005 (article)

PDF [BibTex]

Correlation of EEG spectral entropy with regional cerebral blood flow during sevoflurane and propofol anaesthesia

Maksimow, A., Kaisti, K., Aalto, S., Mäenpää, M., Jääskeläinen, S., Hinkka, S., Martens, SMM., Särkelä, M., Viertiö-Oja, H., Scheinin, H.

Anaesthesia, 60(9):862-869, September 2005 (article)

Abstract

ENTROPY index monitoring, based on spectral entropy of the electroencephalogram, is a promising new method to measure the depth of anaesthesia. We examined the association between spectral entropy and regional cerebral blood flow in healthy subjects anaesthetised with 2%, 3% and 4% end-expiratory concentrations of sevoflurane and 7.6, 12.5 and 19.0 microg.ml(-1) plasma drug concentrations of propofol. Spectral entropy from the frequency band 0.8-32 Hz was calculated and cerebral blood flow assessed using positron emission tomography and [(15)O]-labelled water at baseline and at each anaesthesia level. Both drugs induced significant reductions in spectral entropy and cortical and global cerebral blood flow. Midfrontal-central spectral entropy was associated with individual frontal and whole brain blood flow values across all conditions, suggesting that this novel measure of anaesthetic depth can depict global changes in neuronal activity induced by the drugs. The cortical areas of the most significant associations were remarkably similar for both drugs.

DOI [BibTex]

ei Maksimow, A., Kaisti, K., Aalto, S., Mäenpää, M., Jääskeläinen, S., Hinkka, S., Martens, SMM., Särkelä, M., Viertiö-Oja, H., Scheinin, H. Correlation of EEG spectral entropy with regional cerebral blood flow during sevoflurane and propofol anaesthesia Anaesthesia, 60(9):862-869, September 2005 (article)

DOI [BibTex]

Fast Protein Classification with Multiple Networks

Tsuda, K., Shin, H., Schölkopf, B.

Bioinformatics, 21(Suppl. 2):59-65, September 2005 (article)

Abstract

Support vector machines (SVM) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced Lanckriet et al (2004). In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n^3), and produces a dense matrix of size n x n. We propose an efficient method of protein classification using multiple protein networks. Available protein networks, such as a physical interaction network or a metabolic network, can be directly incorporated. Vectorial data can also be incorporated after conversion into a network by means of neighbor point connection. Similarly to the SDP/SVM method, the combination weights are obtained by convex optimization. Due to the sparsity of network edges, the computation time is nearly linear in the number of edges of the combined network. Additionally, the combination weights provide information useful for discarding noisy or irrelevant networks. Experiments on function prediction of 3588 yeast proteins show promising results: the computation time is enormously reduced, while the accuracy is still comparable to the SDP/SVM method.

PDF Web DOI [BibTex]

ei Tsuda, K., Shin, H., Schölkopf, B. Fast Protein Classification with Multiple Networks Bioinformatics, 21(Suppl. 2):59-65, September 2005 (article)

PDF Web DOI [BibTex]

Analyzing microarray data using quantitative association rules

Georgii, E., Richter, L., Rückert, U., Kramer, S.

Bioinformatics, 21(Suppl. 2):123-129, September 2005 (article)

Abstract

Motivation: We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities. Results: We performed a variety of experiments testing the utility of quantitative association rules for microarray data. First of all, the results should be statistically significant and robust against fluctuations in the data. Next, the approach should be scalable in the number of variables, which is important for such high-dimensional data. Finally, the rules should make sense biologically and be sufficiently different from rules found in regular association rule mining working with discretizations. In all of these dimensions, the proposed approach performed satisfactorily. Therefore, quantitative association rules based on half-spaces should be considered as a tool for the analysis of microarray gene-expression data.

Web DOI [BibTex]

ei Georgii, E., Richter, L., Rückert, U., Kramer, S. Analyzing microarray data using quantitative association rules Bioinformatics, 21(Suppl. 2):123-129, September 2005 (article)

Web DOI [BibTex]

Iterative Kernel Principal Component Analysis for Image Modeling

Kim, K., Franz, M., Schölkopf, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9):1351-1366, September 2005 (article)

Abstract

In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution a nd denoising performance are comparable to existing methods.

Web DOI [BibTex]

ei Kim, K., Franz, M., Schölkopf, B. Iterative Kernel Principal Component Analysis for Image Modeling IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9):1351-1366, September 2005 (article)

Web DOI [BibTex]

Large Margin Methods for Structured and Interdependent Output Variables

Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.

Journal of Machine Learning Research, 6, pages: 1453-1484, September 2005 (article)

Abstract

Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary issue of designing classification algorithms that can deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider problems involving multiple dependent output variables, structured output spaces, and classification problems with class attributes. In order to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains involving different types of output spaces emphasize the breadth and generality of our approach.

PDF [BibTex]

ei Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y. Large Margin Methods for Structured and Interdependent Output Variables Journal of Machine Learning Research, 6, pages: 1453-1484, September 2005 (article)

PDF [BibTex]

Gene Expression Profiling of Serum- and Interleukin-1beta-Stimulated Primary Human Adult Articular Chondrocytes - A Molecular Analysis Based on Chondrocytes Isolated from One Donor

Aigner, T., McKenna, L., Zien, A., Fan, Z., Gebhard, P., Zimmer, R.

Cytokine, 31(3):227-240, August 2005 (article)

Abstract

In order to understand the cellular disease mechanisms of osteoarthritic cartilage degeneration it is of primary importance to understand both the anabolic and the catabolic processes going on in parallel in the diseased tissue. In this study, we have applied cDNA-array technology (Clontech) to study gene expression patterns of primary human normal adult articular chondrocytes isolated from one donor cultured under anabolic (serum) and catabolic (IL-1beta) conditions. Significant differences between the different in vitro cultures tested were detected. Overall, serum and IL-1beta significantly altered gene expression levels of 102 and 79 genes, respectively. IL-1beta stimulated the matrix metalloproteinases-1, -3, and -13 as well as members of its intracellular signaling cascade, whereas serum increased the expression of many cartilage matrix genes. Comparative gene expression analysis with previously published in vivo data (normal and osteoarthritic cartilage) showed significant differences of all in vitro s timulations compared to the changes detected in osteoarthritic cartilage in vivo. This investigation allowed us to characterize gene expression profiles of two classical anabolic and catabolic stimuli of human adult articular chondrocytes in vitro. No in vitro model appeared to be adequate to study overall gene expression alterations in osteoarthritic cartilage. Serum stimulated in vitro cultures largely reflected the results that were only consistent with the anabolic activation seen in osteoarthritic chondrocytes. In contrast, IL-1beta did not appear to be a good model for mimicking catabolic gene alterations in degenerating chondrocytes.

Web [BibTex]

ei Aigner, T., McKenna, L., Zien, A., Fan, Z., Gebhard, P., Zimmer, R. Gene Expression Profiling of Serum- and Interleukin-1beta-Stimulated Primary Human Adult Articular Chondrocytes - A Molecular Analysis Based on Chondrocytes Isolated from One Donor Cytokine, 31(3):227-240, August 2005 (article)

Web [BibTex]

A Combinatorial View of Graph Laplacians

Huang, J.

(144), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2005 (techreport)

Abstract

Discussions about different graph Laplacian, mainly normalized and unnormalized versions of graph Laplacian, have been ardent with respect to various methods in clustering and graph based semi-supervised learning. Previous research on graph Laplacians investigated their convergence properties to Laplacian operators on continuous manifolds. There is still no strong proof on convergence for the normalized Laplacian. In this paper, we analyze different variants of graph Laplacians directly from the ways solving the original graph partitioning problem. The graph partitioning problem is a well-known combinatorial NP hard optimization problem. The spectral solutions provide evidence that normalized Laplacian encodes more reasonable considerations for graph partitioning. We also provide some examples to show their differences.

[BibTex]

ei Huang, J. A Combinatorial View of Graph Laplacians (144), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2005 (techreport)

[BibTex]

Phenotypic characterization of chondrosarcoma-derived cell lines

Schorle, C., Finger, F., Zien, A., Block, J., Gebhard, P., Aigner, T.

Cancer Letters, 226(2):143-154, August 2005 (article)

Abstract

Gene expression profiling of three chondrosarcoma derived cell lines (AD, SM, 105KC) showed an increased proliferative activity and a reduced expression of chondrocytic-typical matrix products compared to primary chondrocytes. The incapability to maintain an adequate matrix synthesis as well as a notable proliferative activity at the same time is comparable to neoplastic chondrosarcoma cells in vivo which cease largely cartilage matrix formation as soon as their proliferative activity increases. Thus, the investigated cell lines are of limited value as substitute of primary chondrocytes but might have a much higher potential to investigate the behavior of neoplastic chondrocytes, i.e. chondrosarcoma biology.

Web [BibTex]

ei Schorle, C., Finger, F., Zien, A., Block, J., Gebhard, P., Aigner, T. Phenotypic characterization of chondrosarcoma-derived cell lines Cancer Letters, 226(2):143-154, August 2005 (article)

Web [BibTex]

Beyond Pairwise Classification and Clustering Using Hypergraphs

Zhou, D., Huang, J., Schölkopf, B.

(143), Max Planck Institute for Biological Cybernetics, August 2005 (techreport)

Abstract

In many applications, relationships among objects of interest are more complex than pairwise. Simply approximating complex relationships as pairwise ones can lead to loss of information. An alternative for these applications is to analyze complex relationships among data directly, without the need to first represent the complex relationships into pairwise ones. A natural way to describe complex relationships is to use hypergraphs. A hypergraph is a graph in which edges can connect more than two vertices. Thus we consider learning from a hypergraph, and develop a general framework which is applicable to classification and clustering for complex relational data. We have applied our framework to real-world web classification problems and obtained encouraging results.

PDF [BibTex]

ei Zhou, D., Huang, J., Schölkopf, B. Beyond Pairwise Classification and Clustering Using Hypergraphs (143), Max Planck Institute for Biological Cybernetics, August 2005 (techreport)

PDF [BibTex]

Local Rademacher Complexities

Bartlett, P., Bousquet, O., Mendelson, S.

The Annals of Statistics, 33(4):1497-1537, August 2005 (article)

Abstract

We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.

PDF PostScript Web [BibTex]

ei Bartlett, P., Bousquet, O., Mendelson, S. Local Rademacher Complexities The Annals of Statistics, 33(4):1497-1537, August 2005 (article)

PDF PostScript Web [BibTex]

Inlier-based ICA with an application to superimposed images

Meinecke, F., Harmeling, S., Müller, K.

International Journal of Imaging Systems and Technology, 15(1):48-55, July 2005 (article)

Abstract

This paper proposes a new independent component analysis (ICA) method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images. Furthermore, the method is designed to be robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers. Our approach is based on a simple outlier index. However, instead of robustifying an existing algorithm by some outlier rejection technique we show how this index can be used directly to solve the ICA problem for super-Gaussian sources. The resulting inlier-based ICA (IBICA) is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed signals).

Web DOI [BibTex]

ei Meinecke, F., Harmeling, S., Müller, K. Inlier-based ICA with an application to superimposed images International Journal of Imaging Systems and Technology, 15(1):48-55, July 2005 (article)

Web DOI [BibTex]

Learning the Kernel with Hyperkernels

Ong, CS., Smola, A., Williamson, R.

Journal of Machine Learning Research, 6, pages: 1043-1071, July 2005 (article)

Abstract

This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional. We state the equivalent representer theorem for the choice of kernels and present a semidefinite programming formulation of the resulting optimization problem. Several recipes for constructing hyperkernels are provided, as well as the details of common machine learning problems. Experimental results for classification, regression and novelty detection on UCI data show the feasibility of our approach.

PDF [BibTex]

ei Ong, CS., Smola, A., Williamson, R. Learning the Kernel with Hyperkernels Journal of Machine Learning Research, 6, pages: 1043-1071, July 2005 (article)

PDF [BibTex]

Generalized Nonnegative Matrix Approximations using Bregman Divergences

Sra, S., Dhillon, I.

Univ. of Texas at Austin, June 2005 (techreport)

[BibTex]

ei Sra, S., Dhillon, I. Generalized Nonnegative Matrix Approximations using Bregman Divergences Univ. of Texas at Austin, June 2005 (techreport)

[BibTex]

Image Reconstruction by Linear Programming

Tsuda, K., Rätsch, G.

IEEE Transactions on Image Processing, 14(6):737-744, June 2005 (article)

Abstract

One way of image denoising is to project a noisy image to the subspace of admissible images derived, for instance, by PCA. However, a major drawback of this method is that all pixels are updated by the projection, even when only a few pixels are corrupted by noise or occlusion. We propose a new method to identify the noisy pixels by l1-norm penalization and to update the identified pixels only. The identification and updating of noisy pixels are formulated as one linear program which can be efficiently solved. In particular, one can apply the upsilon trick to directly specify the fraction of pixels to be reconstructed. Moreover, we extend the linear program to be able to exploit prior knowledge that occlusions often appear in contiguous blocks (e.g., sunglasses on faces). The basic idea is to penalize boundary points and interior points of the occluded area differently. We are also able to show the upsilon property for this extended LP leading to a method which is easy to use. Experimental results demonstrate the power of our approach.

PDF DOI [BibTex]

ei Tsuda, K., Rätsch, G. Image Reconstruction by Linear Programming IEEE Transactions on Image Processing, 14(6):737-744, June 2005 (article)

PDF DOI [BibTex]

RASE: recognition of alternatively spliced exons in C.elegans

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

Bioinformatics, 21(Suppl. 1):i369-i377, June 2005 (article)

PDF Web DOI [BibTex]

ei Rätsch, G., Sonnenburg, S., Schölkopf, B. RASE: recognition of alternatively spliced exons in C.elegans Bioinformatics, 21(Suppl. 1):i369-i377, June 2005 (article)

PDF Web DOI [BibTex]

Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

Tsuda, K., Rätsch, G., Warmuth, M.

Journal of Machine Learning Research, 6, pages: 995-1018, June 2005 (article)

Abstract

We address the problem of learning a symmetric positive definite matrix. The central issue is to design parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Rather than treating the most general case, we focus on two key applications that exemplify our methods: on-line learning with a simple square loss, and finding a symmetric positive definite matrix subject to linear constraints. The updates generalize the exponentiated gradient (EG) update and AdaBoost, respectively: the parameter is now a symmetric positive definite matrix of trace one instead of a probability vector (which in this context is a diagonal positive definite matrix with trace one). The generalized updates use matrix logarithms and exponentials to preserve positive definiteness. Most importantly, we show how the derivation and the analyses of the original EG update and AdaBoost generalize to the non-diagonal case. We apply the resulting matrix exponentiated gradient (MEG) update and DefiniteBoost to the problem of learning a kernel matrix from distance measurements.

PDF [BibTex]

ei Tsuda, K., Rätsch, G., Warmuth, M. Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection Journal of Machine Learning Research, 6, pages: 995-1018, June 2005 (article)

PDF [BibTex]

Measuring Statistical Dependence with Hilbert-Schmidt Norms

Gretton, A., Bousquet, O., Smola, A., Schölkopf, B.

(140), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2005 (techreport)

Abstract

We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

PDF [BibTex]

ei Gretton, A., Bousquet, O., Smola, A., Schölkopf, B. Measuring Statistical Dependence with Hilbert-Schmidt Norms (140), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2005 (techreport)

PDF [BibTex]

Protein function prediction via graph kernels

Borgwardt, KM., Ong, CS., Schönauer, S., Vishwanathan, ., Smola, AJ., Kriegel, H-P.

Bioinformatics, 21(Suppl. 1: ISMB 2005 Proceedings):i47-i56, June 2005 (article)

Abstract

Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs. Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.

PDF Web DOI [BibTex]

ei Borgwardt, KM., Ong, CS., Schönauer, S., Vishwanathan, ., Smola, AJ., Kriegel, H-P. Protein function prediction via graph kernels Bioinformatics, 21(Suppl. 1: ISMB 2005 Proceedings):i47-i56, June 2005 (article)

PDF Web DOI [BibTex]

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