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Overcomplete Blind Source Separation Based on Generalized Gaussian Function and SL0 Norm

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Abstract

This paper is concerned with overcomplete blind source separation using a two-stage sparse representation method. An algorithm incorporating generalized Gaussian function and smoothed \(l_0 \) norm (GGFSL0), which can be viewed as the extension of smoothed \(l_0 \) norm (SL0), is proposed. Compared with previous methods, GGFSL0 tries to directly minimize the \(l_0 \) norm instead of minimizing the \(l_1 \) norm. Numerical experiments with different parameters, sparsity, noise, and dimension demonstrate the effectiveness of proposed algorithm.

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Acknowledgments

The authors would like to thank the anonymous reviewers and the editors for their valuable comments and suggestions. This work is supported by Natural Science Foundation of China (No. 61102103), Youth Foundation of Naval University of Engineering (No. HGDQNJJ13005), Natural Science Foundation of Hubei Province (No. 2013CFB414) and the Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences Wuhan (No. CUGL130247).

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Authors and Affiliations

  1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Rui Qi

  2. School of Science, Naval University of Engineering, Wuhan, 430033, China

    Rui Qi

  3. School of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China

    Yujie Zhang & Hongwei Li

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  1. Rui Qi
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  2. Yujie Zhang
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  3. Hongwei Li
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Correspondence to Rui Qi.

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Qi, R., Zhang, Y. & Li, H. Overcomplete Blind Source Separation Based on Generalized Gaussian Function and SL0 Norm. Circuits Syst Signal Process 34, 2255–2270 (2015). https://doi.org/10.1007/s00034-014-9952-9

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  • DOI: https://doi.org/10.1007/s00034-014-9952-9

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