Divergence based online learning in vector quantization

Authors:
Thomas Villmann;Sven Haase;Frank-Michael Schleif;Barbara Hammer
Affiliations:
University of Applied Sciences Mittweida, Department of Mathematics, Natural Sciences, Informatics;University of Applied Sciences Mittweida, Department of Mathematics, Natural Sciences, Informatics;Clausthal University of Technology, Institute of Computer Science, Clausthal-Zellerfeld, Germany;Clausthal University of Technology, Institute of Computer Science, Clausthal-Zellerfeld, Germany
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

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Abstract

We propose the utilization of divergences in gradient descent learning of supervised and unsupervised vector quantization as an alternative for the squared Euclidean distance. The approach is based on the determination of the Fréchet-derivatives for the divergences, wich can be immediately plugged into the online-learning rules.