A novel measure for quantifying the topology preservation of self-organizing feature maps

Mu Chun Su, Hsiao Te Chang, Chien Hsing Chou

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Recently, feature maps have been applied to various problem domains. The success of some of these applications critically depends on whether feature maps are topologically ordered. In this paper, we propose a novel measure for quantifying the neighborhood preserving property of feature maps. Two data sets were tested to illustrate the performance of the proposed method.

Original languageEnglish
Pages (from-to)137-145
Number of pages9
JournalNeural Processing Letters
Volume15
Issue number2
DOIs
StatePublished - Apr 2002

Keywords

  • Feature maps
  • Neural networks
  • SOM algorithm
  • Topological property

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