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A singular value p-shrinkage thresholding algorithm for low rank matrix recovery

Author

Listed:
  • Yu-Fan Li

    (Sun Yat-Sen University)

  • Kun Shang

    (Hunan University)

  • Zheng-Hai Huang

    (Tianjin University)

Abstract

In this paper, we propose an iterative singular value p-shrinkage thresholding algorithm for solving low rank matrix recovery problem, and also give its two accelerated versions using randomized singular value decomposition. The convergence result of the proposed singular value p-shrinkage thresholding algorithm is proved. Numerical results based on simulation data and real data show the effectiveness of all the three proposed algorithms compared to the existing state-of-the-art algorithms.

Suggested Citation

  • Yu-Fan Li & Kun Shang & Zheng-Hai Huang, 2019. "A singular value p-shrinkage thresholding algorithm for low rank matrix recovery," Computational Optimization and Applications, Springer, vol. 73(2), pages 453-476, June.
    • Handle: RePEc:spr:coopap:v:73:y:2019:i:2:d:10.1007_s10589-019-00084-y
    DOI: 10.1007/s10589-019-00084-y
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2012. "Fast sparse regression and classification," International Journal of Forecasting, Elsevier, vol. 28(3), pages 722-738.
    2. Lingchen Kong & Naihua Xiu, 2013. "EXACT LOW-RANK MATRIX RECOVERY VIA NONCONVEX SCHATTEN p-MINIMIZATION," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 30(03), pages 1-13.
    3. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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    Cited by:

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    2. Yongmei Zhao, 2024. "A Novel Truncated Normal Tensor Completion Method for Multi-Source Fusion Data," Mathematics, MDPI, vol. 12(2), pages 1-14, January.

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