An experimental study: On reducing RBF input dimension by ICA and PCA

Rong Bo Huang*, Lap Tak Law, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

This paper experimentally investigates Indep- endent Component Analysis (ICA) and Principle Component Analysis (PCA) on reducing the input dimension of a Radial Basis Function (RBF) network such that the net's complexity is reduced. The results have shown that a RBF network with ICA as an input pre-process has the similar generalization ability to the one without pre-processing, but the former's performance converges much faster. In contrast, a PCA based RBF however leads to a deteriorated result in both convergent speed and the generalization ability.

Original languageEnglish
Title of host publicationProceedings of 2002 International Conference on Machine Learning and Cybernetics
Pages1941-1945
Number of pages5
Publication statusPublished - 2002
EventProceedings of 2002 International Conference on Machine Learning and Cybernetics - Beijing, China
Duration: 4 Nov 20025 Nov 2002

Publication series

NameProceedings of 2002 International Conference on Machine Learning and Cybernetics
Volume4

Conference

ConferenceProceedings of 2002 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityBeijing
Period4/11/025/11/02

Scopus Subject Areas

  • Engineering(all)

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