Relevance learning in unsupervised vector quantization based on divergences

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