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

Experimentally investigates using independent component analysis (ICA) and principle component analysis (PCA) in the reduction of 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 inpu...

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Bibliographic Details
Published inProceedings. International Conference on Machine Learning and Cybernetics Vol. 4; pp. 1941 - 1945 vol.4
Main Authors Rong-Bo Huang, Lap-Tak Law, Yiu-Ming Cheung
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Computer science
Data mining
Higher order statistics
Image converters
Independent component analysis
Mathematics
Neural networks
Principal component analysis
Radial basis function networks
Signal processing algorithms
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Summary:Experimentally investigates using independent component analysis (ICA) and principle component analysis (PCA) in the reduction of 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 similar generalization ability to the one without pre-processing, but the former's performance converges much faster. In contrast, a PCA based RBF leads to a deteriorated result in both convergent speed and generalization ability.
ISBN:9780780375086
0780375084
DOI:10.1109/ICMLC.2002.1175376