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Relevance learning in unsupervised vector quantization based on divergences

 
: Kästner, M.; Backhaus, A.; Geweniger, T.; Haase, S.; Seiffert, U.; Villmann, T.

:

Laaksonen, J.:
Advances in self-organizing maps. Proceedings : 8th international workshop, WSOM 2011, Espoo, Finland, June 13-15, 2011
Heidelberg: Springer, 2011 (Lecture Notes in Computer Science 6731)
ISBN: 3-642-21565-3
ISBN: 978-3-642-21565-0
ISSN: 0302-9743
pp.90-100
Workshop on Self-Organizing Maps (WSOM) <8, 2011, Espoo>
English
Conference Paper
Fraunhofer IFF ()

Abstract
We propose relevance learning for unsupervised online vector quantization algorithm based on stochastic gradient descent learning according to the given vector quantization cost function. We consider several widely used models including the neural gas algorithm, the Heskes variant of self-organizing maps and the fuzzy c-means. We apply the relevance learning scheme for divergence based similarity measures between prototypes and data vectors in the vector quantization schemes.

: http://publica.fraunhofer.de/documents/N-189805.html