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

Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring input vectors are quantiÿed (or classiÿed) either on the same location or on neighbor ones on a predeÿned 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 artiÿcial and real examples, with speciÿc SOM (ÿxed and decreasing neighborhoods) and SCL algorithms. c 2003 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2007
• Where: CORR
• Authors: Eric de Bodt, Marie Cottrell, Patrick Letrémy, Michel Verleysen
• Keywords: CORR 2007, Education, Topology Preservation Property, Vector Quantization, Vector Quantization Property