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    Tensor N-tubal Rank and Its Convex Relaxation for Low-Rank Tensor Recovery
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         Copyright:  Yu-Bang Zheng, Ting-Zhu Huang, Teng-Yu Ji, 
               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.


