some books on ica

an information-theoretic approach to neural computing, qa76.87.d47 1996 has intros for both information theory and anns, shows connection between pca and ica. has ref to mdl with supervised learning, but it's the old (not stochastic complexity) mdl. independent component analysis: theory and applications, tk5102.9.l44 1998 time-delayed decorrelation, nonlinear ica, some historical info in preface. separated discussions of methods for sub- and super-gaussian (kurtosis < or > gaussian kurtosis) good illustrations of how ica can solve some problems of pca. looks like some deep math, but intelligible advances in independent component analysis, qa76.87.a378 2000 temporal effects: multivariate time series, ref to financial time series; time-varying mixtures, particle filters nonlinear mapping, analyzing independence assumption, ica on noisy data the time-varying stuff really looks interesting independent component analysis: principles and practice, qa76.87.i516 2000 non-stationary sources, including particle filters nonlinear, though not many equations or examples a lot of material seems very similar to the previous book independent component analysis, qa278.h98 2001 lots of background, justification of ica approach preprocessing and time filtering, comparison of algorithms good overall tutorial financial applications independent component analysis: a tutorial introduction, qa76.87.s78 2004 shorter, more intuitive explanations probably better for getting the basic idea kind of dumbed-down in some ways (i know what lambda looks like!) skip the first 6 chapters if you have a decent math background a lot of the ica books seem connected not only with information theory, which i would expect, but also with other stuff on neural nets, which i didn't necessarily expect. this one might be worth checking out. refers to some generalizations of the ica/pca ideas, with good presentation. more stuff on the group's website.