Recovery of Corrupted Low-Rank Tensors

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DOI: 10.4236/am.2017.82019    126 Downloads   188 Views  


This paper studies the problem of recovering low-rank tensors, and the tensors are corrupted by both impulse and Gaussian noise. The problem is well accomplished by integrating the tensor nuclear norm and the l1-norm in a unified convex relaxation framework. The nuclear norm is adopted to explore the low-rank components and the l1-norm is used to exploit the impulse noise. Then, this optimization problem is solved by some augmented-Lagrangian-based algorithms. Some preliminary numerical experiments verify that the proposed method can well recover the corrupted low-rank tensors.

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Fan, H. and Kuang, G. (2017) Recovery of Corrupted Low-Rank Tensors. Applied Mathematics, 8, 229-244. doi: 10.4236/am.2017.82019.


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