Self-adaptive FastICA based on generalized gaussian model

Authors:
Gang Wang;Xin Xu;Dewen Hu
Affiliations:
Department of Automatic Control, National University of Defense Technology, Changsha, Hunan, China;Department of Automatic Control, National University of Defense Technology, Changsha, Hunan, China;Department of Automatic Control, National University of Defense Technology, Changsha, Hunan, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

Activation function is a crucial factor in independent component analysis (ICA) and the best one is the score function defined on the probability density function (pdf) of the source. However, in FastICA, the activation function has to be selected from several predefined choices according to the prior knowledge of the sources, and the problem of how to select or optimize activation function has not been solved yet. In this paper, self-adaptive FastICA is presented based on the generalized Gaussian model (GGM). By combining the optimization of the GGM parameter and that of the demixing vector, a general framework for self-adaptive FastICA is proposed. Convergence and stability of the proposed algorithm are also addressed. Simulation results show that self-adaptive FastICA is effective in parameter optimization and has better accuracy than traditional FastICA.