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code_WSTNN

matlab code


Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery


     Copyright:  Yu-Bang Zheng, Ting-Zhu Huang, Xi-Le Zhao,
                Tai-Xiang Jiang, Teng-Yu Ji, and Tian-Hui Ma

1). Get Started

Run the following Demo_LRTC to compare various methods.

2). Details

More detail can be found in [1]

[1] Y.-B. Zheng, T.-Z. Huang*, X.-L. Zhao, T.-X. Jiang, T.-Y. Ji, and T.-H. Ma,
    Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery.

The compared low-rank tensor completion methods listed as follows:

 1. HaLRTC    [2]    Tucker decomposition based method
 2. TNN       [3]    t-SVD based method
 3. WSTNN     [1]    t-SVD based method

The compared tensor robust principal component analysis methods listed as follows:

 1. SNN       [4]    Tucker decomposition based method
 2. TNN       [5]    Tucker decomposition based method
 3. WSTNN     [1]    t-SVD based method 

3). Citations

[1] Y.-B. Zheng, T.-Z. Huang*, X.-L. Zhao, T.-X. Jiang, T.-Y. Ji, and T.-H. Ma,
    Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery.

[2] J. Liu, P. Musialski, P. Wonka, and J. Ye,
    Tensor completion for estimating missing values in visual data.

[3] Z. Zhang, G. Ely, S. Aeron, N. Hao, and M. Kilmer,
    Novel methods for multilinear data completion and de-noising based on tensor-SVD.

[4] D. Goldfarb and Z. T. Qin,
    Robust low-rank tensor recovery: Models and algorithms.

[5] C. Lu, J. Feng, Y. Chen, W. Liu, Z. Lin, and S. Yan,
    Tensor robust principal component analysis: Exact recovery of corrupted low-rank
    tensors via convex optimization.