Robust Alternating Low-Rank Representation by joint L- and L-norm minimization.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Sep 14, 2017

Abstract

We propose a robust Alternating Low-Rank Representation (ALRR) model formed by an alternating forward-backward representation process. For forward representation, ALRR first recovers the low-rank PCs and random corruptions by an adaptive local Robust PCA (RPCA). Then, ALRR performs a joint L-norm and L-norm minimization (0

Authors

  • Zhao Zhang
  • Mingbo Zhao
    Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.
  • Fanzhang Li
    School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, Jiangsu, China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Shuicheng Yan

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