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

Eric de Bodt (1, 2, 3) , Marie Cottrell (4, 5) , Patrick Letrémy (4, 5) , Michel Verleysen (3)

1 ESA - Ecole Supérieure des Affaires de l'Université Lille 2
2 IAG-FIN - IAG-FIN
3 DICE - MachineLearning Group - DICE
4 SAMOS - Statistique Appliquée et MOdélisation Stochastique
5 MATISSE - UMR 8595 - Modélisation Appliquée, Trajectoires Institutionnelles et Stratégies Socio-Économiques

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.

Mots clés: Vector quantization; Self-organizing maps

Domaines: Statistiques [math.ST]; Réseau de neurones [cs.NE]

Eric de Bodt, Marie Cottrell, Patrick Letrémy, Michel Verleysen. On the use of self-organizing maps to accelerate vector quantization. Neurocomputing, 2004, 56, pp.187-203. ⟨10.1016/j.neucom.2003.09.009⟩. ⟨hal-00122759⟩