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Candes, E.J. and Tao, T. (2010) The Power of Convex Relaxation: Near-Optimal Matrix Completion. IEEE Transaction on Information Theory, 56, 2053-2080.
https://doi.org/10.1109/TIT.2010.2044061

has been cited by the following article:

• TITLE: Recovery of Corrupted Low-Rank Tensors

  AUTHORS: Haiyan Fan, Gangyao Kuang

  KEYWORDS: Low-Rank Tensor, Tensor Recovery, Augmented Lagrangian Method, Impulsive Noise, Mixed Noise

  JOURNAL NAME: Applied Mathematics, Vol.8 No.2, February 28, 2017

  ABSTRACT: 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.