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How to generalize Geometric ICA to higher dimensions

Theis, Fabian J. ; Lang, Elmar



Abstract

The geometric approach to ICA, proposed by Puntonet and Prieto, has one major drawback --- an exponentially rising number of samples and convergence times with increasing dimensiononality --- thus basically restricting geometric ICA to low-dimensional cases. We propose to apply overcomplete ICA to geometric ICA to reduce high-dimensional problems to lower-dimensional ones, thus generalizing geometric ICA to higher dimensions.


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