Skip to main content

Research Repository

Advanced Search

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

Tan, Junpeng; Yang, Zhijing; Ren, Jinchang; Wang, Bing; Cheng, Yongqiang; Ling, Wing Kuen


Junpeng Tan

Zhijing Yang

Jinchang Ren

Yongqiang Cheng

Wing Kuen Ling


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 k-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.


Tan, J., Yang, Z., Ren, J., Wang, B., Cheng, Y., & Ling, W. K. (2022). A Novel Robust Low-rank Multi-view Diversity Optimization Model with Adaptive-Weighting Based Manifold Learning. Pattern Recognition, 122, Article 108298.

Journal Article Type Article
Acceptance Date Aug 31, 2021
Online Publication Date Sep 10, 2021
Publication Date 2022-02
Deposit Date Apr 6, 2022
Publicly Available Date Sep 11, 2022
Journal Pattern Recognition
Print ISSN 0031-3203
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 122
Article Number 108298
Keywords 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)
Public URL


You might also like

Downloadable Citations