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Wright, J., Ganesh, A., Rao, S.Y.P. and Ma, Y. (2009) Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization Neural Information Processing Systems (NIPS).

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.