Convergence properties of SOFM algorithm for vector quantization

Siming Lin, Jennie Si

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present new results to the literature on convergence properties of the self-organizing feature map (SOFM) as a multi-dimensional vector quantizer using Robbins-Monro stochastic approximation principle. It is shown that the weights of the SOFM algorithm converge almost truly to the centroids of the cells of a Voronoi partition of the input space if the neighborhood function satisfies some reasonable conditions. The range of neighborhood functions in the SOFM algorithm is interpreted as a control parameter for an annealing process. Computer simulations were performed to demonstrate the convergence properties of the SOFM.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
Editors Anon
PublisherIEEE
Pages509-512
Number of pages4
Volume1
StatePublished - 1997
EventProceedings of the 1997 IEEE International Symposium on Circuits and Systems, ISCAS'97. Part 4 (of 4) - Hong Kong, Hong Kong
Duration: Jun 9 1997Jun 12 1997

Other

OtherProceedings of the 1997 IEEE International Symposium on Circuits and Systems, ISCAS'97. Part 4 (of 4)
CityHong Kong, Hong Kong
Period6/9/976/12/97

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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