An ICA learning algorithm utilizing geodesic approach

Authors:
Tao Yu;Huai-Zong Shao;Qi-Cong Peng
Affiliations:
UESTC-Texas Instrument DSPs Laboratory, University of Electronic Science and Technology of China, Chengdu, China;UESTC-Texas Instrument DSPs Laboratory, University of Electronic Science and Technology of China, Chengdu, China;UESTC-Texas Instrument DSPs Laboratory, University of Electronic Science and Technology of China, Chengdu, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

This paper presents a novel independent component analysis algorithm that separates mixtures using serially updating geodesic method. The geodesic method is derived from the Stiefel manifold, and an on-line version of this method that can directly treat with the unwhitened observations is obtained. Simulation of artificial data as well as real biological data reveals that our proposed method has fast convergence.