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 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.