Robust auto-weighted projective low-rank and sparse recovery for visual representation

Author:

Wang Lei,Wang Bangjun,Zhang Zhao,Ye Qiaolin,Fu Liyong,Liu Guangcan,Wang Meng

Funder

Central Public-interest Scientific Institution Basal Research Fund

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province of China

Fundamental Research Funds for the Central Universities of China

High-Level Talent of ”Six Talent Peak” Project of Jiangsu Province of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference62 articles.

1. Inductive robust principal component analysis;Bao;IEEE Transaction on Image Processing,2012

2. Laplacian Eigenmaps and spectral techniques for embedding and clustering;Belkin;Neural Information Processing Systems,2002

3. A singular value thresholding algorithm for matrix completion;Cai;SIAM Journal on Control and Optimization,2010

4. Robust principal component analysis?;Candès;Journal of the ACM,2011

5. Recovering low-rank and sparse matrix based on the truncated nuclear norm;Cao;Neural Networks,2017

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