Two adaptive matching learning algorithms for independent component analysis

Authors:
Jinwen Ma;Fei Ge;Dengpan Gao
Affiliations:
Department of Information Science, School of Mathematical Sciences, And LMAM, Peking University, Beijing, China;Department of Information Science, School of Mathematical Sciences, And LMAM, Peking University, Beijing, China;Department of Information Science, School of Mathematical Sciences, And LMAM, Peking University, Beijing, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

Independent component analysis (ICA) has been applied in many fields of signal processing and many ICA learning algorithms have been proposed from different perspectives. However, there is still a lack of a deep mathematical theory to describe the ICA learning algorithm or problem, especially in the cases of both super- and sub-Gaussian sources. In this paper, from the point of view of the one-bit-matching principle, we propose two adaptive matching learning algorithms for the general ICA problem. It is shown by the simulation experiments that the adaptive matching learning algorithms can efficiently solve the ICA problem with both super- and sub-Gaussian sources and outperform the typical existing ICA algorithms in certain aspects.