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Mikio Braun

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6 papers

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YNIMG Journal 2015 Journal Article

Extracting latent brain states — Towards true labels in cognitive neuroscience experiments

Anne K. Porbadnigk
Nico Görnitz
Claudia Sannelli
Alexander Binder
Mikio Braun
Marius Kloft
Klaus-Robert Müller

Neuroscientific data is typically analyzed based on the behavioral response of the participant. However, the errors made may or may not be in line with the neural processing. In particular in experiments with time pressure or studies where the threshold of perception is measured, the error distribution deviates from uniformity due to the structure in the underlying experimental set-up. When we base our analysis on the behavioral labels as usually done, then we ignore this problem of systematic and structured (non-uniform) label noise and are likely to arrive at wrong conclusions in our data analysis. This paper contributes a remedy to this important scenario: we present a novel approach for a) measuring label noise and b) removing structured label noise. We demonstrate its usefulness for EEG data analysis using a standard d2 test for visual attention (N=20 participants).

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JMLR Journal 2015 Journal Article

Fast Cross-Validation via Sequential Testing

Tammo Krueger
Danny Panknin
Mikio Braun

With the increasing size of today's data sets, finding the right parameter configuration in model selection via cross-validation can be an extremely time-consuming task. In this paper we propose an improved cross-validation procedure which uses nonparametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the power of the full cross-validation. Theoretical considerations underline the statistical power of our procedure. The experimental evaluation shows that our method reduces the computation time by a factor of up to 120 compared to a full cross-validation with a negligible impact on the accuracy. [abs] [ pdf ][ bib ] &copy JMLR 2015. ( edit, beta )

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NeurIPS Conference 2010 Conference Paper

Layer-wise analysis of deep networks with Gaussian kernels

Grégoire Montavon
Klaus-Robert Müller
Mikio Braun

Deep networks can potentially express a learning problem more efficiently than local learning machines. While deep networks outperform local learning machines on some problems, it is still unclear how their nice representation emerges from their complex structure. We present an analysis based on Gaussian kernels that measures how the representation of the learning problem evolves layer after layer as the deep network builds higher-level abstract representations of the input. We use this analysis to show empirically that deep networks build progressively better representations of the learning problem and that the best representations are obtained when the deep network discriminates only in the last layers.

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NeurIPS Conference 2006 Conference Paper

Denoising and Dimension Reduction in Feature Space

Mikio Braun
Klaus-Robert Müller
Joachim Buhmann

We show that the relevant information about a classification problem in feature space is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matches the underlying learning problem. Thus, kernels not only transform data sets such that good generalization can be achieved even by linear discriminant functions, but this transformation is also performed in a manner which makes economic use of feature space dimensions. In the best case, kernels provide efficient implicit representations of the data to perform classification. Practically, we propose an algorithm which enables us to recover the subspace and dimensionality relevant for good classification. Our algorithm can therefore be applied (1) to analyze the interplay of data set and kernel in a geometric fashion, (2) to help in model selection, and to (3) de-noise in feature space in order to yield better classification results.

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NeurIPS Conference 2002 Conference Paper

Stability-Based Model Selection

Tilman Lange
Mikio Braun
Volker Roth
Joachim Buhmann

Model selection is linked to model assessment, which is the problem of comparing different models, or model parameters, for a speciﬁc learning task. For supervised learning, the standard practical technique is cross- validation, which is not applicable for semi-supervised and unsupervised settings. In this paper, a new model assessment scheme is introduced which is based on a notion of stability. The stability measure yields an upper bound to cross-validation in the supervised case, but extends to semi-supervised and unsupervised problems. In the experimental part, the performance of the stability measure is studied for model order se- lection in comparison to standard techniques in this area.

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NeurIPS Conference 2001 Conference Paper

The Noisy Euclidean Traveling Salesman Problem and Learning

Mikio Braun
Joachim Buhmann

We consider noisy Euclidean traveling salesman problems in the plane, which are random combinatorial problems with underlying structure. Gibbs sampling is used to compute average trajectories, which estimate the underlying structure common to all instances. This procedure requires identifying the exact relationship between permutations and tours. In a learning setting, the average trajec(cid: 173) tory is used as a model to construct solutions to new instances sampled from the same source. Experimental results show that the average trajectory can in fact estimate the underlying structure and that overfitting effects occur if the trajectory adapts too closely to a single instance.

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