Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering-Reference-Cited by-同舟云学术

Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

Published:2023 Issue: Volume:11 Page:60681-60692
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Khan Mohammad Ahmar¹,Khan Ghufran Ahmad²^ORCID,Khan Jalaluddin²^ORCID,Anwar Taushif²,Ashraf Zubair³^ORCID,Atoum Ibrahim A.⁴^ORCID,Ahmad Naved⁴^ORCID,Shahid Mohammad⁵^ORCID,Ishrat Mohammad²^ORCID,Alghamdi Abdulrahman Abdullah⁶^ORCID

Affiliation:

1. College of Commerce and Business Administration, Dhofar University, Salalah, Oman

2. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India

3. Department of Computer Engineering and Applications, GLA University, Uttar Pradesh, Mathura, India

4. Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia

5. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

6. College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia

Funder

AlMaarefa University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10151858.pdf?arnumber=10151858

Reference49 articles.

1. Self-paced Enhanced Low-rank Tensor Kernelized Multi-view Subspace Clustering

2. Multi-view document clustering based on geometrical similarity measurement

3. Adaptive low-rank kernel block diagonal representation subspace clustering

4. Subspace Clustering by Block Diagonal Representation

5. Multi-view data clustering via non-negative matrix factorization with manifold regularization

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