An efficient independent component analysis algorithm for sub-gaussian sources

Authors:
Zhilin Zhang;Zhang Yi
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 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

A crucial problem for on-line independent component analysis (ICA) algorithm is the choice of step-size, which reflects a tradeoff between steady-state error and convergence speed. This paper proposes a novel ICA algorithm for sub-Gaussian sources, which converges fast while maintaining low steady-state error, since it adopts some techniques, such as the introduction of innovation, usage of skewness information and variable step-size for natural gradient. Simulations have verified these approaches.