scholarly journals Robust Tensor Recovery with Fiber Outliers for Traffic Events

2021 ◽  
Vol 15 (1) ◽  
pp. 1-27
Author(s):  
Yue Hu ◽  
Daniel B. Work
Keyword(s):  
Tensor Recovery

Robust Low-Rank Tensor Recovery via Nonconvex Singular Value Minimization

2020 ◽  
Vol 29 ◽  
pp. 9044-9059
Author(s):  
Lin Chen ◽  
Xue Jiang ◽  
Xingzhao Liu ◽  
Zhixin Zhou
Keyword(s):  
Singular Value ◽  
Low Rank ◽  
Rank Tensor ◽  
Tensor Recovery

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

2020 ◽  
Vol 532 ◽  
pp. 170-189
Author(s):  
Yu-Bang Zheng ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tai-Xiang Jiang ◽  
Teng-Yu Ji ◽  
...  
Keyword(s):  
Convex Relaxation ◽  
Low Rank ◽  
Rank Tensor ◽  
Tensor Recovery

scholarly journals A Splitting Augmented Lagrangian Method for Low Multilinear-Rank Tensor Recovery

2015 ◽  
Vol 32 (01) ◽  
pp. 1540008 ◽  
Author(s):  
Lei Yang ◽  
Zheng-Hai Huang ◽  
Yu-Fan Li
Keyword(s):  
Augmented Lagrangian ◽  
Augmented Lagrangian Method ◽  
Convex Relaxation ◽  
Linear Constraints ◽  
Lagrangian Method ◽  
Splitting Technique ◽  
Completion Problem ◽  
Rank Tensor ◽  
Tensor Recovery ◽  
Multilinear Rank

This paper studies a recovery task of finding a low multilinear-rank tensor that fulfills some linear constraints in the general settings, which has many applications in computer vision and graphics. This problem is named as the low multilinear-rank tensor recovery problem. The variable splitting technique and convex relaxation technique are used to transform this problem into a tractable constrained optimization problem. Considering the favorable structure of the problem, we develop a splitting augmented Lagrangian method (SALM) to solve the resulting problem. The proposed algorithm is easily implemented and its convergence can be proved under some conditions. Some preliminary numerical results on randomly generated and real completion problems show that the proposed algorithm is very effective and robust for tackling the low multilinear-rank tensor completion problem.

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