The Mathematics of Divergence Based Online Learning in Vector Quantization

Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl

OnderzoeksoutputAcademicpeer review

6 Citaten (Scopus)


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. We provide the mathematical foundation of the respective framework. This framework includes usual gradient descent learning of prototypes as well as parameter optimization and relevance learning for improvement of the performance.
Originele taal-2English
TitelArtificial Neural Networks In Pattern Recognition
SubtitelProc. ANNPR 2010
Aantal pagina's12
StatusPublished - 2010

Publicatie series

NaamLecture Notes in Computer Science

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