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A One-Bit-Matching Learning Algorithm for Independent Component Analysis

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Independent Component Analysis and Blind Signal Separation (ICA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3889))

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.

This work was supported by the Natural Science Foundation of China for Project 60471054.

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Author information

Authors and Affiliations

  1. Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China

    Jinwen Ma, Dengpan Gao & Fei Ge

  2. Laboratory of Mathematical Neuroscience, RIKEN Brain Science Institute, Wako-shi, Saitama, 351-0198, Japan

    Jinwen Ma & Shun-ichi Amari

Authors
  1. Jinwen Ma
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  2. Dengpan Gao
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  3. Fei Ge
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  4. Shun-ichi Amari
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Editor information

Editors and Affiliations

  1. Siemens Corporate Research, 755 College Road East, 08540, Princeton, NJ, USA

    Justinian Rosca

  2. Department of CSEE, Oregon Health and Science University, Portland, Oregon, USA

    Deniz Erdogmus

  3. Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA

    José C. Príncipe

  4. McMaster University, 1280 Main Street West, L8S 4K1, Hamilton, Ontario, Canada

    Simon Haykin

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© 2006 Springer-Verlag Berlin Heidelberg

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Ma, J., Gao, D., Ge, F., Amari, Si. (2006). A One-Bit-Matching Learning Algorithm for Independent Component Analysis. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_22

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  • DOI: https://doi.org/10.1007/11679363_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32630-4

  • Online ISBN: 978-3-540-32631-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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