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Tilman Lange

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3 papers

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NeurIPS Conference 2005 Conference Paper

Fusion of Similarity Data in Clustering

Tilman Lange
Joachim Buhmann

Fusing multiple information sources can yield signiﬁcant beneﬁts to suc- cessfully accomplish learning tasks. Many studies have focussed on fus- ing information in supervised learning contexts. We present an approach to utilize multiple information sources in the form of similarity data for unsupervised learning. Based on similarity information, the clustering task is phrased as a non-negative matrix factorization problem of a mix- ture of similarity measurements. The tradeoff between the informative- ness of data sources and the sparseness of their mixture is controlled by an entropy-based weighting mechanism. For the purpose of model se- lection, a stability-based approach is employed to ensure the selection of the most self-consistent hypothesis. The experiments demonstrate the performance of the method on toy as well as real world data sets.

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NeurIPS Conference 2003 Conference Paper

Feature Selection in Clustering Problems

Volker Roth
Tilman Lange

A novel approach to combining clustering and feature selection is pre- sented. It implements a wrapper strategy for feature selection, in the sense that the features are directly selected by optimizing the discrimina- tive power of the used partitioning algorithm. On the technical side, we present an efﬁcient optimization algorithm with guaranteed local con- vergence property. The only free parameter of this method is selected by a resampling-based stability analysis. Experiments with real-world datasets demonstrate that our method is able to infer both meaningful partitions and meaningful subsets of features.

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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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