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

Mu Chun Su; Hsiao Te Chang; Chien Hsing Chou

doi:10.1023/A:1015240802059

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

Mu Chun Su, Hsiao Te Chang, Chien Hsing Chou

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Article › peer-review

3 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 language	English
Pages (from-to)	137-145
Number of pages	9
Journal	Neural Processing Letters
Volume	15
Issue number	2
DOIs	https://doi.org/10.1023/A:1015240802059
State	Published - Apr 2002

Keywords

Feature maps
Neural networks
SOM algorithm
Topological property

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10.1023/A:1015240802059

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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.",

keywords = "Feature maps, Neural networks, SOM algorithm, Topological property",

author = "Su, {Mu Chun} and Chang, {Hsiao Te} and Chou, {Chien Hsing}",

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AU - Chou, Chien Hsing

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KW - Feature maps

KW - Neural networks

KW - SOM algorithm

KW - Topological property

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

Abstract

Keywords

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