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

• Published in: Proceedings. 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
• Language: English
• 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
• DOI: 10.1109/ICMLC.2002.1175376
• ISBN: 9780780375086; 0780375084

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.