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2004


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Asymptotic Properties of the Fisher Kernel

Tsuda, K., Akaho, S., Kawanabe, M., Müller, K.

Neural Computation, 16(1):115-137, 2004 (article)

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PDF [BibTex]

2004


PDF [BibTex]


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Some observations on the effects of slant and texture type on slant-from-texture

Rosas, P., Wichmann, F., Wagemans, J.

Vision Research, 44(13):1511-1535, 2004 (article)

Abstract
We measure the performance of five subjects in a slant-discrimination task for differently textured planes. As textures we used uniform lattices, randomly displaced lattices, circles (polka dots), Voronoi tessellations, plaids, 1/f noise, “coherent” noise and a leopard skin-like texture. Our results show: (1) Improving performance with larger slants for all textures. (2) Thus, following from (1), cases of “non-symmetrical” performance around a particular orientation. (3) For orientations sufficiently slanted, the different textures do not elicit major differences in performance, (4) while for orientations closer to the vertical plane there are marked differences between them. (5) These differences allow a rank-order of textures to be formed according to their “helpfulness”– that is, how easy the discrimination task is when a particular texture is mapped on the plane. Polka dots tend to allow the best slant discrimination performance, noise patterns the worst. Two additional experiments were conducted to test the generality of the obtained rank-order. First, the tilt of the planes was rotated to break the axis of gravity present in the original discrimination experiment. Second, the task was changed to a slant report task via probe adjustment. The results of both control experiments confirmed the texture-based rank-order previously obtained. We comment on the importance of these results for depth perception research in general, and in particular the implications our results have for studies of cue combination (sensor fusion) using texture as one of the cues involved.

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PDF [BibTex]

PDF [BibTex]


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A kernel view of the dimensionality reduction of manifolds

Ham, J., Lee, D., Mika, S., Schölkopf, B.

In Proceedings of the Twenty-First International Conference on Machine Learning, pages: 369-376, (Editors: CE Brodley), ACM, New York, NY, USA, ICML, 2004, also appeared as MPI-TR 110 (inproceedings)

Abstract
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local neighborhood information to construct a global embedding of the manifold. We show how all three algorithms can be described as kernel PCA on specially constructed Gram matrices, and illustrate the similarities and differences between the algorithms with representative examples.

ei

PDF [BibTex]

PDF [BibTex]


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Protein Functional Class Prediction with a Combined Graph

Shin, H., Tsuda, K., Schölkopf, B.

In Proceedings of the Korean Data Mining Conference, pages: 200-219, Proceedings of the Korean Data Mining Conference, 2004 (inproceedings)

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 protein-protein 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 single graph.

ei

PDF [BibTex]

PDF [BibTex]


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Learning from Labeled and Unlabeled Data Using Random Walks

Zhou, D., Schölkopf, B.

In Pattern Recognition, Proceedings of the 26th DAGM Symposium, pages: 237-244, (Editors: Rasmussen, C.E., H.H. Bülthoff, M.A. Giese and B. Schölkopf), Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

Abstract
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels of the unlabeled points. Any supervised learning algorithm can be applied to this problem, for instance, Support Vector Machines (SVMs). The problem of our interest is if we can implement a classifier which uses the unlabeled data information in some way and has higher accuracy than the classifiers which use the labeled data only. Recently we proposed a simple algorithm, which can substantially benefit from large amounts of unlabeled data and demonstrates clear superiority to supervised learning methods. In this paper we further investigate the algorithm using random walks and spectral graph theory, which shed light on the key steps in this algorithm.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Protein homology detection using string alignment kernels

Saigo, H., Vert, J., Ueda, N., Akutsu, T.

Bioinformatics, 20(11):1682-1689, 2004 (article)

Abstract
Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVM) are currently the most effective methods for the problem of superfamily recognition in the SCOP database. The performance of SVMs depend critically on the kernel function used to quantify the similarity between sequences. We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the art methods for remote homology detection.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data

Tsuda, K., Uda, S., Kin, T., Asai, K.

Neural Processing Letters, 19, pages: 63-72, 2004 (article)

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PDF [BibTex]

PDF [BibTex]


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A Tutorial on Support Vector Regression

Smola, A., Schölkopf, B.

Statistics and Computing, 14(3):199-222, 2004 (article)

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Web [BibTex]

Web [BibTex]


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Multivariate Regression via Stiefel Manifold Constraints

BakIr, G., Gretton, A., Franz, M., Schölkopf, B.

In Lecture Notes in Computer Science, Vol. 3175, pages: 262-269, (Editors: CE Rasmussen and HH Bülthoff and B Schölkopf and MA Giese), Springer, Berlin, Germany, Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

Abstract
We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered independently. By contrast, in our approach the feature construction and the regression estimation are performed jointly, directly minimizing a loss function that we specify, subject to a rank constraint. A major advantage of this approach is that the loss is no longer chosen according to the algorithmic requirements, but can be tailored to the characteristics of the task at hand; the features will then be optimal with respect to this objective, and dependence between the outputs can be exploited.

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PostScript [BibTex]

PostScript [BibTex]


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Implicit estimation of Wiener series

Franz, M., Schölkopf, B.

In Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop, pages: 735-744, (Editors: A Barros and J Principe and J Larsen and T Adali and S Douglas), IEEE, New York, Machine Learning for Signal Processing XIV, Proc. IEEE Signal Processing Society Workshop, 2004 (inproceedings)

Abstract
The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear systems. We propose an implicit estimation method based on regression in a reproducing kernel Hilbert space that alleviates these problems. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Hilbertian Metrics on Probability Measures and their Application in SVM’s

Hein, H., Lal, T., Bousquet, O.

In Pattern Recognition, Proceedings of th 26th DAGM Symposium, 3175, pages: 270-277, Lecture Notes in Computer Science, (Editors: Rasmussen, C. E., H. H. Bülthoff, M. Giese and B. Schölkopf), Pattern Recognition, Proceedings of th 26th DAGM Symposium, 2004 (inproceedings)

Abstract
The goal of this article is to investigate the field of Hilbertian metrics on probability measures. Since they are very versatile and can therefore be applied in various problems they are of great interest in kernel methods. Quit recently Tops{o}e and Fuglede introduced a family of Hilbertian metrics on probability measures. We give basic properties of the Hilbertian metrics of this family and other used metrics in the literature. Then we propose an extension of the considered metrics which incorporates structural information of the probability space into the Hilbertian metric. Finally we compare all proposed metrics in an image and text classification problem using histogram data.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Gasussian process model based predictive control

Kocijan, J., Murray-Smith, R., Rasmussen, CE., Girard, A.

In Proceedings of the ACC 2004, pages: 2214-2219, Proceedings of the ACC, 2004 (inproceedings)

Abstract
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identi cation of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coef cients to be optimised. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.

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PDF PostScript [BibTex]

PDF PostScript [BibTex]


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A New Variational Framework for Rigid-Body Alignment

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

In Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004), pages: 171-179, (Editors: Fred, A.,T. Caelli, R.P.W. Duin, A. Campilho and D. de Ridder), Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR) and Statistical Pattern Recognition (SPR), 2004 (inproceedings)

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PDF [BibTex]

PDF [BibTex]


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Practical Method for Blind Inversion of Wiener Systems

Zhang, K., Chan, L.

In Proceedings of International Joint Conference on Neural Networks (IJCNN 2004), pages: 2163-2168, International Joint Conference on Neural Networks (IJCNN), 2004, Volume 3 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Bayesian analysis of the Scatterometer Wind Retrieval Inverse Problem: Some New Approaches

Cornford, D., Csato, L., Evans, D., Opper, M.

Journal of the Royal Statistical Society B, 66, pages: 1-17, 3, 2004 (article)

Abstract
The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem.A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters.We show how Gaussian process priors can be used efficiently with a variety of likelihood models, using local forward (observation) models and direct inverse models for the scatterometer.We present an enhanced Markov chain Monte Carlo method to sample from the resulting multimodal posterior distribution.We go on to show how the computational complexity of the inference can be controlled by using a sparse, sequential Bayes algorithm for estimation with Gaussian processes.This helps to overcome the most serious barrier to the use of probabilistic, Gaussian process methods in remote sensing inverse problems, which is the prohibitively large size of the data sets.We contrast the sampling results with the approximations that are found by using the sparse, sequential Bayes algorithm.

ei

PDF [BibTex]

PDF [BibTex]


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Feature Selection for Support Vector Machines Using Genetic Algorithms

Fröhlich, H., Chapelle, O., Schölkopf, B.

International Journal on Artificial Intelligence Tools (Special Issue on Selected Papers from the 15th IEEE International Conference on Tools with Artificial Intelligence 2003), 13(4):791-800, 2004 (article)

ei

Web [BibTex]

Web [BibTex]


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Semi-supervised kernel regression using whitened function classes

Franz, M., Kwon, Y., Rasmussen, C., Schölkopf, B.

In Pattern Recognition, Proceedings of the 26th DAGM Symposium, Lecture Notes in Computer Science, Vol. 3175, LNCS 3175, pages: 18-26, (Editors: CE Rasmussen and HH Bülthoff and MA Giese and B Schölkopf), Springer, Berlin, Gerrmany, 26th DAGM Symposium, 2004 (inproceedings)

Abstract
The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique that results in an implicit whitening of the basis functions by penalizing directions in function space with a large prior variance. The regularization term is computed from unlabelled input data that characterizes the input distribution. Tests on two datasets using polynomial basis functions showed an improved average performance compared to standard ridge regression.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O.

In Learning Theory and Kernel Machines, pages: 72-86, (Editors: Schölkopf, B. and Warmuth, M. K.), Springer, Heidelberg, Germany, 16. Annual Conference on Computational Learning Theory / COLT Kernel, 2004 (inproceedings)

Abstract
In this article we construct a maximal margin classification algorithm for arbitrary metric spaces. At first we show that the Support Vector Machine (SVM) is a maximal margin algorithm for the class of metric spaces where the negative squared distance is conditionally positive definite (CPD). This means that the metric space can be isometrically embedded into a Hilbert space, where one performs linear maximal margin separation. We will show that the solution only depends on the metric, but not on the kernel. Following the framework we develop for the SVM, we construct an algorithm for maximal margin classification in arbitrary metric spaces. The main difference compared with SVM is that we no longer embed isometrically into a Hilbert space, but a Banach space. We further give an estimate of the capacity of the function class involved in this algorithm via Rademacher averages. We recover an algorithm of Graepel et al. [6].

ei

PDF PostScript PDF DOI [BibTex]

PDF PostScript PDF DOI [BibTex]


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On the Convergence of Spectral Clustering on Random Samples: The Normalized Case

von Luxburg, U., Bousquet, O., Belkin, M.

In Proceedings of the 17th Annual Conference on Learning Theory, pages: 457-471, Proceedings of the 17th Annual Conference on Learning Theory, 2004 (inproceedings)

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Optical Imaging of the Spatiotemporal Dynamics of Cerebral Blood Flow and Oxidative Metabolism in the Rat Barrel Cortex

Weber, B., Burger, C., Wyss, M., von Schulthess, G., Scheffold, F., Buck, A.

European Journal of Neuroscience, 20(10):2664-2670, 2004 (article)

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PDF [BibTex]

PDF [BibTex]


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Phenotypic Characterization of Human Chondrocyte Cell Line C-20/A4: A Comparison between Monolayer and Alginate Suspension Culture

Finger, F., Schorle, C., Söder, S., Zien, A., Goldring, M., Aigner, T.

Cells Tissues Organs, 178(2):65-77, 2004 (article)

Abstract
DNA microarray analysis was used to investigate the molecular phenotype of one of the first human chondrocyte cell lines, C-20/A4, derived from juvenile costal chondrocytes by immortalization with origin-defective simian virus 40 large T antigen. Clontech Human Cancer Arrays 1.2 and quantitative PCR were used to examine gene expression profiles of C-20/A4 cells cultured in the presence of serum in monolayer and alginate beads. In monolayer cultures, genes involved in cell proliferation were strongly upregulated compared to those expressed by human adult articular chondrocytes in primary culture. Of the cell cycle-regulated genes, only two, the CDK regulatory subunit and histone H4, were downregulated after culture in alginate beads, consistent with the ability of these cells to proliferate in suspension culture. In contrast, the expression of several genes that are involved in pericellular matrix formation, including MMP-14, COL6A1, fibronectin, biglycan and decorin, was upregulated when the C-20/A4 cells were transferred to suspension culture in alginate. Also, nexin-1, vimentin, and IGFBP-3, which are known to be expressed by primary chondrocytes, were differentially expressed in our study. Consistent with the proliferative phenotype of this cell line, few genes involved in matrix synthesis and turnover were highly expressed in the presence of serum. These results indicate that immortalized chondrocyte cell lines, rather than substituting for primary chondrocytes, may serve as models for extending findings on chondrocyte function not achievable by the use of primary chondrocytes.

ei

[BibTex]

[BibTex]


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Kernel Methods and their Potential Use in Signal Processing

Perez-Cruz, F., Bousquet, O.

IEEE Signal Processing Magazine, (Special issue on Signal Processing for Mining), 2004 (article) Accepted

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PostScript [BibTex]

PostScript [BibTex]


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Learning Movement Primitives

Schaal, S., Peters, J., Nakanishi, J., Ijspeert, A.

In 11th International Symposium on Robotics Research (ISRR2003), pages: 561-572, (Editors: Dario, P. and Chatila, R.), Springer, ISRR, 2004, clmc (inproceedings)

Abstract
This paper discusses a comprehensive framework for modular motor control based on a recently developed theory of dynamic movement primitives (DMP). DMPs are a formulation of movement primitives with autonomous nonlinear differential equations, whose time evolution creates smooth kinematic control policies. Model-based control theory is used to convert the outputs of these policies into motor commands. By means of coupling terms, on-line modifications can be incorporated into the time evolution of the differential equations, thus providing a rather flexible and reactive framework for motor planning and execution. The linear parameterization of DMPs lends itself naturally to supervised learning from demonstration. Moreover, the temporal, scale, and translation invariance of the differential equations with respect to these parameters provides a useful means for movement recognition. A novel reinforcement learning technique based on natural stochastic policy gradients allows a general approach of improving DMPs by trial and error learning with respect to almost arbitrary optimization criteria. We demonstrate the different ingredients of the DMP approach in various examples, involving skill learning from demonstration on the humanoid robot DB, and learning biped walking from demonstration in simulation, including self-improvement of the movement patterns towards energy efficiency through resonance tuning.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]