An Experimental Study: on Reducing Rbf Input Dimension by Ica and Pca
نویسنده
چکیده
This paper experimentally investigates Independent 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 of convergent speed and the generalization ability.
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