ica and svd

been poking around some more with svds, and came across this presentation that refers to independent component analysis. the wikipedia article is decent, with good refs. one way to view the ica is as a nonlinear generalization of pca; the components are not necessarily orthogonal but they minimize the mutual information and all the higher-order cross cumulants, rather than just the second like pca. the conditions of uncorrelated and independent are equivalent only for gaussion random vars, but ica has determinacy problems of more than one of the components are gaussian. this ebook has good info on it in chapter 6, if you can get access.