Prototype Proliferation in the Growing Neural Gas Algorithm

Authors:
Héctor F. Satizábal;Andres Pérez-Uribe;Marco Tomassini
Affiliations:
Institut des Systèmes d'Information (ISI), Université de Lausanne, Hautes Etudes Commerciales (HEC), and University of Applied Sciences of Western Switzerland (HEIG-VD)(REDS), and Corpor ...;University of Applied Sciences of Western Switzerland (HEIG-VD)(REDS),;Institut des Systèmes d'Information (ISI), Université de Lausanne, Hautes Etudes Commerciales (HEC),
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

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Abstract

Growing Neural Gas is an incremental vector quantization algorithm with the capabilities of topology-preserving and distribution-matching. Distribution matching can produce overpopulation of prototypes in zones with high density of data. In order to tackle this drawback, we introduce some modifications to the original Growing Neural Gas algorithm by adding three new parameters, one of them controlling the distribution of the codebook and the other two controlling the quantization error and the amount of units in the network. The resulting learning algorithm is capable of efficiently quantizing datasets presenting high and low density regions while solving the prototype proliferation problem.