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.