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

Rong Bo Huang; Lap Tak Law; Yiu Ming Cheung

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

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-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 language	English
Title of host publication	Proceedings of 2002 International Conference on Machine Learning and Cybernetics
Pages	1941-1945
Number of pages	5
Publication status	Published - 2002
Event	2002 International Conference on Machine Learning and Cybernetics - Beijing, China Duration: 4 Nov 2002 → 5 Nov 2002

Publication series

Name	Proceedings of 2002 International Conference on Machine Learning and Cybernetics
Volume	4

Conference

Conference	2002 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Beijing
Period	4/11/02 → 5/11/02

Scopus Subject Areas

Engineering(all)

Cite this

@inproceedings{b718789c923d4da1a80b10c2286959aa,

title = "An experimental study: On reducing RBF input dimension by ICA and PCA",

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.",

author = "Huang, {Rong Bo} and Law, {Lap Tak} and Cheung, {Yiu Ming}",

note = "Copyright: Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.; 2002 International Conference on Machine Learning and Cybernetics ; Conference date: 04-11-2002 Through 05-11-2002",

year = "2002",

language = "English",

isbn = "0780375084",

series = "Proceedings of 2002 International Conference on Machine Learning and Cybernetics",

pages = "1941--1945",

booktitle = "Proceedings of 2002 International Conference on Machine Learning and Cybernetics",

}

Huang, RB, Law, LT & Cheung, YM 2002, An experimental study: On reducing RBF input dimension by ICA and PCA. in Proceedings of 2002 International Conference on Machine Learning and Cybernetics. Proceedings of 2002 International Conference on Machine Learning and Cybernetics, vol. 4, pp. 1941-1945, 2002 International Conference on Machine Learning and Cybernetics, Beijing, China, 4/11/02.

An experimental study : On reducing RBF input dimension by ICA and PCA. / Huang, Rong Bo; Law, Lap Tak; Cheung, Yiu Ming.

Proceedings of 2002 International Conference on Machine Learning and Cybernetics. 2002. p. 1941-1945 (Proceedings of 2002 International Conference on Machine Learning and Cybernetics; Vol. 4).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

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AU - Huang, Rong Bo

AU - Law, Lap Tak

AU - Cheung, Yiu Ming

PY - 2002

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N2 - 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.

AB - 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.

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AN - SCOPUS:0036926754

SN - 0780375084

T3 - Proceedings of 2002 International Conference on Machine Learning and Cybernetics

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EP - 1945

BT - Proceedings of 2002 International Conference on Machine Learning and Cybernetics

Y2 - 4 November 2002 through 5 November 2002

ER -

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

Abstract

Publication series

Conference

Scopus Subject Areas

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