Learning vector quantization classification with local relevance determination for medical data

Authors:
B. Hammer;T. Villmann;F.-M. Schleif;C. Albani;W. Hermann
Affiliations:
Institute of Computer Science, Clausthal University of Technology, Clausthal-Zellerfeld, Germany;Clinic for Psychotherapy, University of Leipzig, Leipzig, Germany;Institute of Computer Science, University of Leipzig, Germany;Clinic for Psychotherapy, University of Leipzig, Leipzig, Germany;Paracelsus Hospital Zwickau, Germany
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

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Abstract

In this article we extend the global relevance learning vector quantization approach by local metric adaptation to obtain a locally optimized model for classification. In this sense we make a step in the direction of quadratic discriminance analysis in statistics where classwise variance matrices are used for class adapted discriminance functions. We demonstrateb the performance of the model for a medical application.