TY  - JOUR
A1  - Zien, Alexander
A1  - Rätsch, Gunnar
A1  - Mika, Sebastian
A1  - Schölkopf, Bernhard
A1  - Lengauer, Thomas
A1  - Müller, Klaus-Robert
T1  - Engineering support vector machine kernels that recognize translation initiation sites
Y1  - 2000
SN  - 1367-4803
ER  - 
TY  - JOUR
A1  - Ziehe, Andreas
A1  - Müller, Klaus-Robert
A1  - Nolte, G.
A1  - Mackert, B.-M.
A1  - Curio, Gabriel
T1  - Artifact reduction in magnetoneurography based on time-delayed second-order correlations
Y1  - 2000
ER  - 
TY  - JOUR
A1  - Ziehe, Andreas
A1  - Laskov, Pavel
A1  - Nolte, G
A1  - Müller, Klaus-Robert
T1  - A fast algorithm for joint diagonalization with non-orthogonal transformations and its application to blind source separation
N2  - A new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm is based on the Frobenius-norm formulation of the joint diagonalization problem, and addresses diagonalization with a general, non- orthogonal transformation. The iterative scheme of the algorithm is based on a multiplicative update which ensures the invertibility of the diagonalizer. The algorithm's efficiency stems from the special approximation of the cost function resulting in a sparse, block-diagonal Hessian to be used in the computation of the quasi-Newton update step. Extensive numerical simulations illustrate the performance of the algorithm and provide a comparison to other leading diagonalization methods. The results of such comparison demonstrate that the proposed algorithm is a viable alternative to existing state-of-the-art joint diagonalization algorithms. The practical use of our algorithm is shown for blind source separation problems
Y1  - 2004
ER  - 
TY  - JOUR
A1  - Wübbeler, G.
A1  - Ziehe, Andreas
A1  - Mackert, B.-M.
A1  - Müller, Klaus-Robert
A1  - Trahms, L.
A1  - Curio, Gabriel
T1  - Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans
Y1  - 2000
ER  - 
TY  - BOOK
A1  - Tsuda, Koji
A1  - Sugiyama, Masashi
A1  - Müller, Klaus-Robert
T1  - Subspace information criterion for non-quadratice regularizers : model selection for sparse regressors
T3  - GMD-Report
Y1  - 2000
VL  - 120
PB  - GMD-Forschungszentrum Informationstechnik
CY  - Sankt Augustin
ER  - 
TY  - JOUR
A1  - Sugiyama, Masashi
A1  - Kawanabe, Motoaki
A1  - Müller, Klaus-Robert
T1  - Trading variance reduction with unbiasedness : the regularized subspace information criterion for robust model selection in kernel regression
N2  - A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results
Y1  - 2004
SN  - 0899-7667
ER  - 
TY  - JOUR
A1  - Shenoy, Pradeep
A1  - Krauledat, Matthias
A1  - Blankertz, Benjamin
A1  - Rao, Rajesh P. N.
A1  - Müller, Klaus-Robert
T1  - Towards adaptive classification for BCI
N2  - Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain- computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-) stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance
Y1  - 2006
UR  - http://iopscience.iop.org/1741-2552/3/1/R02/
U6  - https://doi.org/10.1088/1741-2560/3/1/R02
ER  - 
TY  - JOUR
A1  - Rätsch, Gunnar
A1  - Schölkopf, B.
A1  - Smola, Alexander J.
A1  - Müller, Klaus-Robert
A1  - Mika, Sebastian
T1  - V-Arc : ensemble learning in the preence of outliers
Y1  - 2000
ER  - 
TY  - JOUR
A1  - Rätsch, Gunnar
A1  - Schölkopf, B.
A1  - Smola, Alexander J.
A1  - Mika, Sebastian
A1  - Onoda, T.
A1  - Müller, Klaus-Robert
T1  - Robust ensemble learning
Y1  - 2000
SN  - 0-262-19448-1
ER  - 
TY  - JOUR
A1  - Rätsch, Gunnar
A1  - Schölkopf, B.
A1  - Smola, Alexander J.
A1  - Mika, Sebastian
A1  - Onoda, T.
A1  - Müller, Klaus-Robert
T1  - Robust ensemble learning for data analysis
Y1  - 2000
ER  - 
TY  - BOOK
A1  - Rätsch, Gunnar
A1  - Schölkopf, B.
A1  - Mika, Sebastian
A1  - Müller, Klaus-Robert
T1  - SVM and boosting : one class
T3  - GMD-Report
Y1  - 2000
VL  - 119
PB  - GMD-Forschungszentrum Informationstechnik
CY  - Sankt Augustin
ER  - 
TY  - JOUR
A1  - Parra, L.
A1  - Spence, C.
A1  - Sajda, P.
A1  - Ziehe, Andreas
A1  - Müller, Klaus-Robert
T1  - Unmixing hyperspectral data
Y1  - 2000
ER  - 
TY  - JOUR
A1  - Onoda, T.
A1  - Rätsch, Gunnar
A1  - Müller, Klaus-Robert
T1  - An asymptotic analysis and improvement of AdaBoost in the binary classification case (in Japanese)
Y1  - 2000
ER  - 
TY  - JOUR
A1  - Nolte, Guido
A1  - Meinecke, Frank C.
A1  - Ziehe, Andreas
A1  - Müller, Klaus-Robert
T1  - Identifying interactions in mixed and noisy complex systems
N2  - We present a technique that identifies truly interacting subsystems of a complex system from multichannel data if the recordings are an unknown linear and instantaneous mixture of the true sources. The method is valid for arbitrary noise structure. For this, a blind source separation technique is proposed that diagonalizes antisymmetrized cross- correlation or cross-spectral matrices. The resulting decomposition finds truly interacting subsystems blindly and suppresses any spurious interaction stemming from the mixture. The usefulness of this interacting source analysis is demonstrated in simulations and for real electroencephalography data
Y1  - 2006
UR  - http://pre.aps.org/
U6  - https://doi.org/10.1103/Physreve.73.051913
ER  - 
TY  - JOUR
A1  - Müller, Klaus-Robert
A1  - Vigario, R.
A1  - Meinecke, Frank C.
A1  - Ziehe, Andreas
T1  - Blind source separation techniques for decomposing event-related brain signals
N2  - Recently blind source separation (BSS) methods have been highly successful when applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of event-related MEG measurements. In a first experiment we apply BSS to artifact identification of raw MEG data and discuss how the quality of the resulting independent component projections can be evaluated. The second part of our study considers averaged data of event-related magnetic fields. Here, it is particularly important to monitor and thus avoid possible overfitting due to limited sample size. A stability assessment of the BSS decomposition allows to solve this task and an additional grouping of the BSS components reveals interesting structure, that could ultimately be used for gaining a better physiological modeling of the data
Y1  - 2004
SN  - 0218-1274
ER  - 
TY  - JOUR
A1  - Montavon, Gregoire
A1  - Braun, Mikio L.
A1  - Krüger, Tammo
A1  - Müller, Klaus-Robert
T1  - Analyzing local structure in Kernel-Based learning
JF  - IEEE signal processing magazine
Y1  - 2013
U6  - https://doi.org/10.1109/MSP.2013.2249294
SN  - 1053-5888
VL  - 30
IS  - 4
SP  - 62
EP  - 74
PB  - Inst. of Electr. and Electronics Engineers
CY  - Piscataway
ER  - 
TY  - JOUR
A1  - Mika, Sebastian
A1  - Rätsch, Gunnar
A1  - Weston, J.
A1  - Schölkopf, B.
A1  - Smola, Alexander J.
A1  - Müller, Klaus-Robert
T1  - Invariant feature extraction and classification in kernel spaces
Y1  - 2000
ER  - 
TY  - JOUR
A1  - Mieth, Bettina
A1  - Kloft, Marius
A1  - Rodriguez, Juan Antonio
A1  - Sonnenburg, Soren
A1  - Vobruba, Robin
A1  - Morcillo-Suarez, Carlos
A1  - Farre, Xavier
A1  - Marigorta, Urko M.
A1  - Fehr, Ernst
A1  - Dickhaus, Thorsten
A1  - Blanchard, Gilles
A1  - Schunk, Daniel
A1  - Navarro, Arcadi
A1  - Müller, Klaus-Robert
T1  - Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies
JF  - Scientific reports
N2  - The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.
Y1  - 2016
U6  - https://doi.org/10.1038/srep36671
SN  - 2045-2322
VL  - 6
PB  - Nature Publ. Group
CY  - London
ER  - 
TY  - JOUR
A1  - Meinecke, Frank C.
A1  - Ziehe, Andreas
A1  - Kurths, Jürgen
A1  - Müller, Klaus-Robert
T1  - Measuring phase synchronization of superimposed signals
N2  - Phase synchronization is an important phenomenon that occurs in a wide variety of complex oscillatory processes. Measuring phase synchronization can therefore help to gain fundamental insight into nature. In this Letter we point out that synchronization analysis techniques can detect spurious synchronization, if they are fed with a superposition of signals such as in electroencephalography or magnetoencephalography data. We show how techniques from blind source separation can help to nevertheless measure the true synchronization and avoid such pitfalls
Y1  - 2005
SN  - 0031-9007
ER  - 
TY  - JOUR
A1  - Meinecke, Frank C.
A1  - Harmeling, Stefan
A1  - Müller, Klaus-Robert
T1  - Inlier-based ICA with an application to superimposed images
N2  - 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). (c) 2005 Wiley Periodicals, Inc
Y1  - 2005
SN  - 0899-9457
ER  -