Efficient and Effective Nonconvex Low-Rank Subspace Clustering via SVT-Free Operators-Reference-Cited by-同舟云学术

Efficient and Effective Nonconvex Low-Rank Subspace Clustering via SVT-Free Operators

Published:2023-12 Issue:12 Volume:33 Page:7515-7529
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Zhang Hengmin¹^ORCID,Li Shuyi²,Qiu Jing³^ORCID,Tang Yang⁴^ORCID,Wen Jie⁵^ORCID,Zha Zhiyuan¹^ORCID,Wen Bihan¹^ORCID

Affiliation:

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore

2. Faculty of Information Technology, College of Information and Communication Engineering, Beijing University of Technology, Beijing, China

3. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

4. Department of Automation and also with the Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

5. Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

National Key Research and Development Program of China

Major Key Project of Pengcheng Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/10348119/10123026.pdf?arnumber=10123026

Reference72 articles.

1. Enhancing Sparsity by Reweighted ℓ 1 Minimization

2. A Survey of Sparse Representation: Algorithms and Applications

3. Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset

4. Low Rank Regularization: A review

5. Low rank subspace clustering (LRSC)

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