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Neural Computation, Vol 10, 2103-2114, Copyright © 1998 by The MIT Press

LETTERS

An Alternative Perspective on Adaptive Independent Component Analysis Algorithms

Mark Girolami

This article develops an extended independent component analysis algorithm for mixtures of arbitrary subgaussian and supergaussian sources. The gaussian mixture model of Pearson is employed in deriving a closed-form generic score function for strictly subgaussian sources. This is combined with the score function for a unimodal supergaussian density to provide a computationally simple yet powerful algorithm for performing independent component analysis on arbitrary mixtures of nongaussian sources.


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