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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

A Novel Robust Low-rank Multi-view Diversity Optimization Model with Adaptive-Weighting Based Manifold Learning

Than, Junpeng, Yang, Zhijing, Ren, Jinchang, Wang, Bing, Cheng, Yongqiang and Ling, Wing-Kuen (2021) A Novel Robust Low-rank Multi-view Diversity Optimization Model with Adaptive-Weighting Based Manifold Learning. Pattern Recognition, 122. ISSN 0031-3203

Item Type: Article


Multi-view clustering has become a hot yet challenging topic, due mainly to the independence of and information complementarity between different views. Although good results are achieved to a certain extent from typical methods including multi-view based -means clustering, sparse cooperative representation clustering and subspace clustering, they still suffer from several drawbacks or limitations: (1) When each view is sparse decomposed, it still contains some hidden information for mining, such as the structure of samples, the intra-class similarity measure, and the inter-class diversity discrimination, etc. (2) Most of the existing multi-view methods only consider the local features within each view, but fail to effectively balance the importance of and combine information among different views in a diversified way. To tackle these issues, we propose a novel multi-view diversity learning model based on robust bilinear error decomposition (BED). The BED term with a low rank sparse constraint is an improved non-negative matrix factorization (NMF), which is used to extract the hidden structure information in sparse decomposition and useful diversity discrimination information in error matrix. The preservation of local features and selection of important views are achieved by adaptive weighted manifold learning. Furthermore, the Hilbert Schmidt independence criterion is used as a diversity learning term for mutual learning and fusion among views. Finally, the proposed robust low-rank multi-view diversity learning spectral clustering method is evaluated and benchmarked with eight state-of-the-art methods. Experiments in six real datasets have fully validated the significantly improved accuracy and efficiency of the proposed methodology for effective clustering of multi-view images.

PR-D-20-00324_R2 (author).pdf - Accepted Version

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Uncontrolled Keywords: Low-rank Representation (LRR) Multi-view Subspace Clustering (MVSC) Hilbert Schmidt Independence Criterion (HSIC) Non-negative Matrix Factorization (NMF) Adaptive-Weighting Manifold Learning (AWML)
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Depositing User: Yongqiang Cheng


Item ID: 16816
Identification Number:
ISSN: 0031-3203
Official URL:

Users with ORCIDS

ORCID for Yongqiang Cheng: ORCID iD

Catalogue record

Date Deposited: 11 Jan 2024 12:15
Last Modified: 11 Jan 2024 12:30


Author: Yongqiang Cheng ORCID iD
Author: Junpeng Than
Author: Zhijing Yang
Author: Jinchang Ren
Author: Bing Wang
Author: Wing-Kuen Ling

University Divisions

Faculty of Technology > School of Computer Science


Computing > Data Science
Computing > Artificial Intelligence

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