Fuzzy classification using information theoretic learning vector quantization

Authors:
Thomas Villmann;Barbara Hammer;Frank-Michael Schleif;Wieland Hermann;Marie Cottrell
Affiliations:
Medical Department, Universität Leipzig, Germany;Institute of Computer Science, Clausthal University of Technology, Germany;Medical Department, Universität Leipzig, Germany;Paracelsus-Klinik Zwickau, Germany;University Paris I Sorbonne-Panthéon, SAMOS, France
Venue:
Neurocomputing
Year:
2008

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Abstract

In this article we extend the (recently published) unsupervised information theoretic vector quantization approach based on the Cauchy-Schwarz-divergence for matching data and prototype densities to supervised learning and classification. In particular, first we generalize the unsupervised method to more general metrics instead of the Euclidean, as it was used in the original algorithm. Thereafter, we extend the model to a supervised learning method resulting in a fuzzy classification algorithm. Thereby, we allow fuzzy labels for both, data and prototypes. Finally, we transfer the idea of relevance learning for metric adaptation known from learning vector quantization to the new approach. We show the abilities and the power of the method for exemplary and real-world medical applications.