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2016


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Decision-Making under Ambiguity Is Modulated by Visual Framing, but Not by Motor vs. Non-Motor Context: Experiments and an Information-Theoretic Ambiguity Model

Grau-Moya, J, Ortega, PA, Braun, DA

PLoS ONE, 11(4):1-21, April 2016 (article)

Abstract
A number of recent studies have investigated differences in human choice behavior depending on task framing, especially comparing economic decision-making to choice behavior in equivalent sensorimotor tasks. Here we test whether decision-making under ambiguity exhibits effects of task framing in motor vs. non-motor context. In a first experiment, we designed an experience-based urn task with varying degrees of ambiguity and an equivalent motor task where subjects chose between hitting partially occluded targets. In a second experiment, we controlled for the different stimulus design in the two tasks by introducing an urn task with bar stimuli matching those in the motor task. We found ambiguity attitudes to be mainly influenced by stimulus design. In particular, we found that the same subjects tended to be ambiguity-preferring when choosing between ambiguous bar stimuli, but ambiguity-avoiding when choosing between ambiguous urn sample stimuli. In contrast, subjects’ choice pattern was not affected by changing from a target hitting task to a non-motor context when keeping the stimulus design unchanged. In both tasks subjects’ choice behavior was continuously modulated by the degree of ambiguity. We show that this modulation of behavior can be explained by an information-theoretic model of ambiguity that generalizes Bayes-optimal decision-making by combining Bayesian inference with robust decision-making under model uncertainty. Our results demonstrate the benefits of information-theoretic models of decision-making under varying degrees of ambiguity for a given context, but also demonstrate the sensitivity of ambiguity attitudes across contexts that theoretical models struggle to explain.

ei

DOI [BibTex]


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Austauschgekoppelte Moden in magnetischen Vortexstrukturen

Dieterle, G.

Universität Stuttgart, Stuttgart, 2016 (phdthesis)

mms

[BibTex]

[BibTex]


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Density matrix calculations for the ultrafast demagnetization after femtosecond laser pulses

Weng, Weikai

Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

mms

[BibTex]

[BibTex]


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Outlook and challenges for hydrogen storage in nanoporous materials

Broom, D. P., Webb, C. J., Hurst, K. E., Parilla, P. A., Gennett, T., Brown, C. M., Zacharia, R., Tylianakis, E., Klontzas, E., Froudakis, G. E., Steriotis, T. A., Trikalitis, P. N., Anton, D. L., Hardy, B., Tamburello, D., Corgnale, C., van Hassel, B. A., Cossement, D., Chahine, R., Hirscher, M.

{Applied Physics A}, 122(3), Springer-Verlag Heidelberg, Heidelberg, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Quantum sieving for separation of hydrogen isotopes using MOFs

Oh, H., Hirscher, M.

{European Journal of Inorganic Chemistry}, 2016(27):4278-4289, Wiley-VCH, Weinheim, Germany, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Direct patterning of vortex generators on a fiber tip using a focused ion beam

Vayalamkuzhi, P., Bhattacharya, S., Eigenthaler, U., Keskinbora, K., Salman, C. T., Hirscher, M., Spatz, J. P., Viswanathan, N. K.

{Optics Letters}, 41(10):2133-2136, Optical Society of America, Washington, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Two-body problem of core-region coupled magnetic vortex stacks

Hänze, M., Adolff, C. F., Velten, S., Weigand, M., Meier, G.

{Physical Review B}, 93(5), American Physical Society, Woodbury, NY, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Irreproducibility in hydrogen storage material research

Broom, D. P., Hirscher, M.

{Energy \& Environmental Science}, 9(11):3368-3380, Royal Society of Chemistry, Cambridge, UK, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Effect of surface configurations on the room-temperature magnetism of pure ZnO

Chen, Y., Wang, Z., Leineweber, A., Baier, J., Tietze, T., Phillipp, F., Schütz, G., Goering, E.

{Journal of Materials Chemistry C}, 4(19):4166-4175, Royal Society of Chemistry, London, UK, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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On the synthesis and microstructure analysis of high performance MnBi

Chen, Y., Sawatzki, S., Ener, S., Sepehri-Amin, H., Leineweber, A., Gregori, G., Qu, F., Muralidhar, S., Ohkubo, T., Hono, K., Gutfleisch, O., Kronmüller, H., Schütz, G., Goering, E.

{AIP Advances}, 6(12), 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Deep Learning for Diabetic Retinopathy Diagnostics

Balles, Lukas

Heidelberg University, 2016 (mastersthesis)

[BibTex]

[BibTex]


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The role of individual defects on the magnetic screening of HTSC films

Ruoß, S., Stahl, C., Weigand, M., Zahn, P., Bayer, J., Schütz, G., Albrecht, J.

{New Journal of Physics}, 18(10), IOP Publishing, Bristol, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Magnetic switching of nanoscale antidot lattices

Wiedwald, U., Gräfe, J., Lebecki, K. M., Skripnik, M., Haering, F., Schütz, G., Ziemann, P., Goering, E., Nowak, U.

{Beilstein Journal of Nanotechnology}, 7, pages: 733-750, Beilstein-Institut, Frankfurt am Main, 2016 (article)

mms

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Hydrogen-based energy storage (IEA-HIA Task 32)

Buckley, C. E., Chen, P., van Hassel, B. A., Hirscher, M.

{Applied Physics A}, 122(2), Springer-Verlag Heidelberg, Heidelberg, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Local domain-wall velocity engineering via tailored potential landscapes in ferromagnetic rings

Richter, K., Krone, A., Mawass, M., Krüger, B., Weigand, M., Stoll, H., Schütz, G., Kläui, M.

{Physical Review Applied}, 5(2), American Physical Society, College Park, Md. [u.a.], 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Geometric control of the magnetization reversal in antidot lattices with perpendicular magnetic anisotropy

Gräfe, J., Weigand, M., Träger, N., Schütz, G., Goering, E. J., Skripnik, M., Nowak, U., Haering, F., Ziemann, P., Wiedwald, U.

{Physical Review B}, 93(10), American Physical Society, Woodbury, NY, 2016 (article)

mms

DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


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Growth and characterizationof large weak topological insulator Bi2Tel single crystal by Bismuth self-flux method

Ryu, G., Son, K., Schütz, G.

{Journal of Crystal Growth}, 440, pages: 26-30, North-Holland, Amsterdam, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Additive interfacial chiral interaction in multilayers for stabilization of small individual skyrmions at room temperature

Moreau-Luchaire, C., Moutafis, C., Reyren, N., Sampaio, J., Vaz, C. A. F., Van Horne, N., Bouzehouane, K., Garcia, K., Deranlot, C., Warnicke, P., Wohlhüter, P., George, J.-M., Weigand, M., Raabe, J., Cros, V., Fert, A.

{Nature Nanotechnology}, 11(5):444-448, Nature Publishing Group, London, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Surface defect free growth of a spin dimer TlCuCl3 compound crystals and investigations on its optical and magnetic properties

Ryu, G., Son, K.

{Journal of Solid State Chemistry}, 237, pages: 358-363, Academic Press, Orlando, Fla., 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Physical and mathematical justification of the numerical Brillouin zone integration of the Boltzmann rate equation by Gaussian smearing

Illg, C., Haag, M., Teeny, N., Wirth, J., Fähnle, M.

{Journal of Theoretical and Applied Physics}, 10(1):1-6, Springer, Berlin, Heidelberg, Tehran, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Helium und Hydrogen Isotope Adsorption and Separation in Metal-Organic Frameworks

Zaiser, Ingrid

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

mms

[BibTex]

[BibTex]


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Pinned orbital moments - A new contribution to magnetic anisotropy

Audehm, P., Schmidt, M., Brück, S., Tietze, T., Gräfe, J., Macke, S., Schütz, G., Goering, E.

{Scientific Reports}, 6, Nature Publishing Group, London, UK, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Comparative study of ALD SiO2 thin films for optical applications

Pfeiffer, K., Shestaeva, S., Bingel, A., Munzert, P., Ghazaryan, L., van Helvoirt, C., Kessels, W. M. M., Sanli, U. T., Grévent, C., Schütz, G., Putkonen, M., Buchanan, I., Jensen, L., Ristau, D., Tünnermann, A., Szeghalmi, A.

{Optical materials express}, 6(2):660-670, OSA, Washington, DC, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Combined first-order reversal curve and x-ray microscopy investigation of magnetization reversal mechanisms in hexagonal antidot lattices

Gräfe, J., Weigand, M., Stahl, C., Träger, N., Kopp, M., Schütz, G., Goering, E. J., Haering, F., Ziemann, P., Wiedwald, U.

{Physical Review B}, 93(1), American Physical Society, Woodbury, NY, 2016 (article)

mms

DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


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Switching probabilities of magnetic vortex core reversal studied by table top magneto optic Kerr microscopy

Dieterle, G., Gangwar, A., Gräfe, J., Noske, M., Förster, J., Woltersdorf, G., Stoll, H., Back, C. H., Schütz, G.

{Applied Physics Letters}, 108(2), American Institute of Physics, Melville, NY, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Ultrafast demagnetization after femtosecond laser pulses: Transfer of angular momentum from the electronic system to magnetoelastic spin-phonon modes

Tsatsoulis, T., Illg, C., Haag, M., Müller, B. Y., Zhang, L., Fähnle, M.

{Physical Review B}, 93(13), American Physical Society, Woodbury, NY, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Developments in the Ni-Nb-Zr amorphous alloy membranes

Sarker, S., Chandra, D., Hirscher, M., Dolan, M., Isheim, D., Wermer, J., Viano, D., Baricco, M., Udovic, T. J., Grant, D., Palumbo, O., Paolone, A., Cantelli, R.

{Applied Physics A}, 122(3), Springer-Verlag Heidelberg, Heidelberg, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Resistance to the transport of H2 through the external surface of as-made and modified silicalite-1 (MFI)

Kalantzopoulos, G. N., Policicchio, A., Maccallini, E., Krkljus, I., Ciuchi, F., Hirscher, M., Agostino, R. G., Golemme, G.

{Microporous and Mesoporous Materials}, 220, pages: 290-297, Elsevier, Amsterdam, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Observation of pseudopartial grain boundary wetting in the NdFeB-based alloy

Straumal, B. B., Mazilkin, A. A., Protasova, S. G., Schütz, G., Straumal, A. B., Baretzky, B.

{Journal of Materials Engineering and Performance}, 25(8):3303-3309, 2016 (article)

mms

DOI [BibTex]

DOI [BibTex]

2005


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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.

ei

PDF PostScript PDF [BibTex]

2005


PDF PostScript PDF [BibTex]


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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.

ei

PDF [BibTex]

PDF [BibTex]


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Extension to Kernel Dependency Estimation with Applications to Robotics

BakIr, G.

Biologische Kybernetik, Technische Universität Berlin, Berlin, November 2005 (phdthesis)

Abstract
Kernel Dependency Estimation(KDE) is a novel technique which was designed to learn mappings between sets without making assumptions on the type of the involved input and output data. It learns the mapping in two stages. In a first step, it tries to estimate coordinates of a feature space representation of elements of the set by solving a high dimensional multivariate regression problem in feature space. Following this, it tries to reconstruct the original representation given the estimated coordinates. This thesis introduces various algorithmic extensions to both stages in KDE. One of the contributions of this thesis is to propose a novel linear regression algorithm that explores low-dimensional subspaces during learning. Furthermore various existing strategies for reconstructing patterns from feature maps involved in KDE are discussed and novel pre-image techniques are introduced. In particular, pre-image techniques for data-types that are of discrete nature such as graphs and strings are investigated. KDE is then explored in the context of robot pose imitation where the input is a an image with a human operator and the output is the robot articulated variables. Thus, using KDE, robot pose imitation is formulated as a regression problem.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Geometrical aspects of statistical learning theory

Hein, M.

Biologische Kybernetik, Darmstadt, Darmstadt, November 2005 (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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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.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Selective integration of multiple biological data for supervised network inference

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

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

ei

PDF [BibTex]

PDF [BibTex]


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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.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Implicit Surfaces For Modelling Human Heads

Steinke, F.

Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, September 2005 (diplomathesis)

ei

[BibTex]

[BibTex]


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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.

ei

PDF [BibTex]

PDF [BibTex]


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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.

ei

PDF [BibTex]

PDF [BibTex]


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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.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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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.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Machine Learning Methods for Brain-Computer Interdaces

Lal, TN.

Biologische Kybernetik, University of Darmstadt, September 2005 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


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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.

ei

Web [BibTex]

Web [BibTex]


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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.

ei

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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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.

ei

PDF [BibTex]

PDF [BibTex]


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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.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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RASE: recognition of alternatively spliced exons in C.elegans

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

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

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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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.

ei

PDF [BibTex]

PDF [BibTex]


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Texture and haptic cues in slant discrimination: Reliability-based cue weighting without statistically optimal cue combination

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

Journal of the Optical Society of America A, 22(5):801-809, May 2005 (article)

Abstract
A number of models of depth cue combination suggest that the final depth percept results from a weighted average of independent depth estimates based on the different cues available. The weight of each cue in such an average is thought to depend on the reliability of each cue. In principle, such a depth estimation could be statistically optimal in the sense of producing the minimum variance unbiased estimator that can be constructed from the available information. Here we test such models using visual and haptic depth information. Different texture types produce differences in slant discrimination performance, providing a means for testing a reliability-sensitive cue combination model using texture as one of the cues to slant. Our results show that the weights for the cues were generally sensitive to their reliability, but fell short of statistically optimal combination—we find reliability-based re-weighting, but not statistically optimal cue combination.

ei

PDF Web [BibTex]

PDF Web [BibTex]