Low-rank and sparse matrix decomposition via the truncated nuclear norm and a sparse regularizer-Reference-Cited by-同舟云学术

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Low-rank and sparse matrix decomposition via the truncated nuclear norm and a sparse regularizer

Published:2018-05-24 Issue:11 Volume:35 Page:1549-1566
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Xue Zhichao,Dong Jing^ORCID,Zhao Yuxin,Liu Chang,Chellali Ryad

Funder

Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China

National Natural Science Foundation of China

Major Basic Research Program for National Security of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s00371-018-1555-1.pdf

Reference57 articles.

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2. Bhardwaj, A., Raman, S.: Robust PCA-based solution to image composition using augmented lagrange multiplier (ALM). Vis. Comput. 32(5), 591–600 (2016)

3. Bouwmans, T., Sobral, A., Javed, S., Jung, S.K., Zahzah, E.H.: Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset. Comput. Sci. Rev. 23, 1–71 (2017)

4. Bouwmans, T., Zahzah, E.H.: Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance. Comput. Vis. Image Underst. 122, 22–34 (2014)

5. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)

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