Hyper-Laplacian Regularized Nonconvex Low-Rank Representation for Multi-View Subspace Clustering-Reference-Cited by-同舟云学术

Hyper-Laplacian Regularized Nonconvex Low-Rank Representation for Multi-View Subspace Clustering

Published:2022 Issue: Volume:8 Page:376-388
ISSN:2373-776X
Container-title:IEEE Transactions on Signal and Information Processing over Networks
language:
Short-container-title:IEEE Trans. on Signal and Inf. Process. over Networks

Author:

Wang Shuqin¹^ORCID,Chen Yongyong²^ORCID,Zhang Linna³,Cen Yigang¹^ORCID,Voronin Viacheslav⁴^ORCID

Affiliation:

1. Institute of Information Science, Beijing Jiaotong University and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China

2. Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China

3. School of Mechanical Engineering, Guizhou University, Guiyang, China

4. Center for Cognitive Technology and Machine Vision, Moscow State University of Technology “STANKIN,”, Moscow, Russia

Funder

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Beijing Municipal Natural Science Foundation

Russian Foundation for Basic Research

Shenzhen College Stability Support Plan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6884276/9666472/09763010.pdf?arnumber=9763010

Reference62 articles.

1. Cross-view Locality Preserved Diversity and Consensus Learning for Multi-view Unsupervised Feature Selection

2. Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification

3. Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications

4. Low-Rank Quaternion Approximation for Color Image Processing

5. Robust PCA Using Matrix Factorization for Background/Foreground Separation

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