PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient variant of algorithm FastICA for independent component analysis attaining the Cramer-Rao lower bound.
Petr Tichavsky, Zbynek Koldovski and Erkki Oja
IEEE Transactions on Neural Networks Volume 17, Number 5, pp. 1265-1277, 2006.

Abstract

An efficient variant of the algorithm FastICA for independent component analysis is derived, that attains the theoretically minimal Cramer-Rao lower bound. Thus the algorithm is asymptotically as efficient as possible.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:1800
Deposited By:Erkki Oja
Deposited On:28 November 2005