A new fixed-point algorithm for independent component analysis

doi:10.1016/j.neucom.2003.09.002

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A new fixed-point algorithm for independent component analysis

2004-01-01

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Zhenwei Shi

Huanwen Tang

Yi‐Yuan Tang

DOI：10.1016/j.neucom.2003.09.002

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Abstract

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A new fixed-point algorithm for independent component analysis (ICA) is presented that is able blindly to separate mixed signals with sub- and super-Gaussian source distributions. The new fixed-point algorithm maximizes the likelihood of the ICA model under the constraint of decorrelation and uses the method of Lee et al. (Neural Comput. 11(2) (1999) 417) to switch between sub- and super-Gaussian regimes. The new fixed-point algorithm maximizes the likelihood very fast and reliably. The validity of this algorithm is confirmed by the simulations and experimental results. (C) 2003 Elsevier B.V. All rights reserved.

Keywords:

independent component analysis

blind source separation

fixed-point algorithm

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