Robust Alternating Low-Rank Representation by joint L p - and L 2,p -norm minimization.
Autor: | Zhang Z; School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou 215006, China. Electronic address: cszzhang@gmail.com., Zhao M; Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong., Li F; School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou 215006, China., Zhang L; School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou 215006, China., Yan S; Department of Electrical and Computer Engineering, National University of Singapore, Singapore. |
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Jazyk: | angličtina |
Zdroj: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2017 Dec; Vol. 96, pp. 55-70. Date of Electronic Publication: 2017 Sep 14. |
DOI: | 10.1016/j.neunet.2017.08.001 |
Abstrakt: | 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 p -norm on the coefficients can ensure joint sparsity for subspace representation, while the L |
Databáze: | MEDLINE |
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