Functional relevance learning in generalized learning vector quantization

Authors:
Marika Kästner;Barbara Hammer;Michael Biehl;Thomas Villmann
Affiliations:
University of Applied Sciences Mittweida, Computational Intelligence Group, Technikumplatz 17, 09648 Mittweida, Germany;University Bielefeld, Center of Excellence - Cognitive Interaction Technology CITEC, Universitätsstrasse 21-23, 33615 Bielefeld, Germany;University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science, P.O. Box 407, 9700 AK Groningen, The Netherlands;University of Applied Sciences Mittweida, Computational Intelligence Group, Technikumplatz 17, 09648 Mittweida, Germany
Venue:
Neurocomputing
Year:
2012

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Abstract

Relevance learning in learning vector quantization is a central paradigm for classification task depending feature weighting and selection. We propose a functional approach to relevance learning for high-dimensional functional data. For this purpose we compose the relevance profile by a superposition of only a few parametrized basis functions taking into account the functional character of the data. The number of these parameters is usually significantly smaller than the number of relevance weights in standard relevance learning, which is the number of data dimensions. Thus, instabilities in learning are avoided and an inherent regularization takes place. In addition, we discuss strategies to obtain sparse relevance models for further model optimization.