Contrast functions for independent subspace analysis

Authors:
Jason A. Palmer;Scott Makeig
Affiliations:
Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA;Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

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Abstract

We consider the Independent Subspace Analysis problem from the point of view of contrast functions, showing that contrast functions are able to partially solve the ISA problem. That is, basic ICA can solve the ISA problem up to within-subspace separation/analysis. We define sub- and super-Gaussian subspaces and extend to ISA a previous result on freedom of ICA from local optima. We also consider new types of dependent densities that satisfy or violate the entropy power inequality (EPI) condition.