A Novel Measure for Quantifying the Topology Preservation of Self-Organizing Feature Maps

Authors:
Mu-Chun Su;Hsiao-Te Chang;Chien-Hsing Chou
Affiliations:
Department of Computer Science and Information Engineering, National Central University, Taiwan, R.O.C.;Department of Electrical Engineering, Tamkang University, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, National Central University, Taiwan, R.O.C.
Venue:
Neural Processing Letters
Year:
2002

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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.