On the use of self-organizing maps to accelerate vector quantization

Details On the use of self-organizing maps to accelerate vector quantization On the use of self-organizing maps to accelerate vector quantization

2007-01-04

arxiv.org/abs/math/0701142v1

Neurocomputing 56 (2004) 187-203

Mathematics

Statistics Theory

A la suite de la conference ESANN 1999

Scientific paper

10.1016/j.neucom.2003.09.009

Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring input vectors are quantified (or classified) either on the same location or on neighbor ones on a predefined grid. SOM are also widely used for their more classical vector quantization property. We show in this paper that using SOM instead of the more classical Simple Competitive Learning (SCL) algorithm drastically increases the speed of convergence of the vector quantization process. This fact is demonstrated through extensive simulations on artificial and real examples, with specific SOM (fixed and decreasing neighborhoods) and SCL algorithms.

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