Robustness Meets Low-Rankness: Unified Entropy and Tensor Learning for Multi-View Subspace Clustering-Reference-Cited by-同舟云学术

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Robustness Meets Low-Rankness: Unified Entropy and Tensor Learning for Multi-View Subspace Clustering

Published:2023-11 Issue:11 Volume:33 Page:6302-6316
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:

Wang Shuqin¹^ORCID,Chen Yongyong²^ORCID,Lin Zhiping³^ORCID,Cen Yigang¹^ORCID,Cao Qi⁴^ORCID

Affiliation:

1. Institute of Information Science, Beijing Jiaotong University, Beijing, China

2. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

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

4. School of Computing Science, University of Glasgow, Glasgow, Singapore

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Guangdong Natural Science Foundation

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/10297951/10102285.pdf?arnumber=10102285

Reference56 articles.

1. Robust and Efficient Subspace Segmentation via Least Squares Regression

2. Robust Recovery of Subspace Structures by Low-Rank Representation

3. Low-Rank Tensor Graph Learning for Multi-View Subspace Clustering

4. Low-Rank Tensor Constrained Multiview Subspace Clustering

5. Large-Scale Multi-View Subspace Clustering in Linear Time

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Subspace-Contrastive Multi-View Clustering;ACM Transactions on Knowledge Discovery from Data;2024-06-28

2. Landmark-based k-factorization multi-view subspace clustering;Information Sciences;2024-05

3. Bi-Nuclear Tensor Schatten-p Norm Minimization for Multi-View Subspace Clustering;IEEE Transactions on Image Processing;2023

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