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A New Bayesian Method for Jointly Sparse Signal Recovery

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

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Abstract

In this paper, we address the recovery of a set of jointly sparse vectors from incomplete measurements. We provide a Bayesian inference scheme for the multiple measurement vector model and develop a novel method to carry out maximum a posteriori estimation for the Bayesian inference based on the prior information on the sparsity structure. Instead of implementing Bayesian variables estimation, we establish the corresponding minimization algorithms for all of the sparse vectors by applying block coordinate descent techniques, and then solve them iteratively and sequently through a re-weighted method. Numerical experiments demonstrate the enhancement of joint sparsity via the new method and its robust recovery performance in the case of a low sampling ratio.

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China

    Haiyan Yang, Xiaolin Huang, Cheng Peng & Jie Yang

  2. Department of Automation, Tsinghua University, Beijing, China

    Li Li

Authors
  1. Haiyan Yang
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  2. Xiaolin Huang
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  3. Cheng Peng
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  4. Jie Yang
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  5. Li Li
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Corresponding author

Correspondence to Xiaolin Huang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Yang, H., Huang, X., Peng, C., Yang, J., Li, L. (2017). A New Bayesian Method for Jointly Sparse Signal Recovery. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_94

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  • DOI: https://doi.org/10.1007/978-3-319-70093-9_94

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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