A one-bit-matching learning algorithm for independent component analysis

Authors:
Jinwen Ma;Dengpan Gao;Fei Ge;Shun-ichi Amari
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;Laboratory of Mathematical Neuroscience, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

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Abstract

Independent component analysis (ICA) has many practical applications in the fields of signal and image processing and several ICA learning algorithms have been constructed via the selection of model probability density functions. However, there is still a lack of deep mathematical theory to validate these ICA algorithms, especially for the general case that super- and sub-Gaussian sources coexist. In this paper, according to the one-bit-matching principle and by turning the de-mixing matrix into an orthogonal matrix via certain normalization, we propose a one-bit-matching ICA learning algorithm on the Stiefel manifold. It is shown by the simulated and audio experiments that our proposed learning algorithm works efficiently on the ICA problem with both super- and sub-Gaussian sources and outperforms the extended Infomax and Fast-ICA algorithms.