BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clustering
Multi-view subspace clustering leverages complementary information from different views to uncover latent subspaces. However, incomplete multi-view data is prevalent, particularly in fields such as communication systems. Incomplete Multi-View Subspace Clustering (IMSC) addresses this challenge but f...
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001164 |
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| author | Jiaqiyu Zhan Yuesheng Zhu Guibo Luo |
| author_facet | Jiaqiyu Zhan Yuesheng Zhu Guibo Luo |
| author_sort | Jiaqiyu Zhan |
| collection | DOAJ |
| description | Multi-view subspace clustering leverages complementary information from different views to uncover latent subspaces. However, incomplete multi-view data is prevalent, particularly in fields such as communication systems. Incomplete Multi-View Subspace Clustering (IMSC) addresses this challenge but faces two main challenges: (1) neglecting dissimilarities between views and samples, and (2) insufficient handling of cross-view consistency. To tackle these issues, we propose a novel IMSC framework, referred to as Bi-Adaptive and Cross-View Consistency (BACVC). BACVC improves incomplete data recovery and subspace structure discovery through view-adaptive tensor rank constraints, data-adaptive high-order correlations, and view-level contrastive learning. Specifically, we first apply tensor factorization with view-adaptive tensor rank approximation to enforce low-rank constraints on a stacked affinity tensor, capturing the view-specific subspace block-diagonal structure. We then introduce a data-adaptive non-uniform hypergraph-induced hyper-Laplacian regularization to model high-order correlations and guide the recovery of incomplete data. Finally, contrastive learning is applied to the soft clustering assignment of each view, ensuring cross-view structural consistency. Extensive experiments on four benchmark datasets show that BACVC outperforms eleven state-of-the-art methods, with improvements of up to 4.39%, 5.43%, and 3.95% in ACC, NMI, and purity, respectively. Experimental results demonstrate the robustness of BACVC in handling incomplete data and its effectiveness in practical applications. |
| format | Article |
| id | doaj-art-24b1bd8e2c304776ad3fa7f400bba885 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-24b1bd8e2c304776ad3fa7f400bba8852025-02-12T05:30:43ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119623633BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clusteringJiaqiyu Zhan0Yuesheng Zhu1Guibo Luo2School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, ChinaCorresponding author.; School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, ChinaSchool of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, ChinaMulti-view subspace clustering leverages complementary information from different views to uncover latent subspaces. However, incomplete multi-view data is prevalent, particularly in fields such as communication systems. Incomplete Multi-View Subspace Clustering (IMSC) addresses this challenge but faces two main challenges: (1) neglecting dissimilarities between views and samples, and (2) insufficient handling of cross-view consistency. To tackle these issues, we propose a novel IMSC framework, referred to as Bi-Adaptive and Cross-View Consistency (BACVC). BACVC improves incomplete data recovery and subspace structure discovery through view-adaptive tensor rank constraints, data-adaptive high-order correlations, and view-level contrastive learning. Specifically, we first apply tensor factorization with view-adaptive tensor rank approximation to enforce low-rank constraints on a stacked affinity tensor, capturing the view-specific subspace block-diagonal structure. We then introduce a data-adaptive non-uniform hypergraph-induced hyper-Laplacian regularization to model high-order correlations and guide the recovery of incomplete data. Finally, contrastive learning is applied to the soft clustering assignment of each view, ensuring cross-view structural consistency. Extensive experiments on four benchmark datasets show that BACVC outperforms eleven state-of-the-art methods, with improvements of up to 4.39%, 5.43%, and 3.95% in ACC, NMI, and purity, respectively. Experimental results demonstrate the robustness of BACVC in handling incomplete data and its effectiveness in practical applications.http://www.sciencedirect.com/science/article/pii/S1110016825001164Multi-view subspace clusteringIncomplete multi-view dataHigh-order correlationTensor factorizationContrastive learning |
| spellingShingle | Jiaqiyu Zhan Yuesheng Zhu Guibo Luo BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clustering Alexandria Engineering Journal Multi-view subspace clustering Incomplete multi-view data High-order correlation Tensor factorization Contrastive learning |
| title | BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clustering |
| title_full | BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clustering |
| title_fullStr | BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clustering |
| title_full_unstemmed | BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clustering |
| title_short | BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clustering |
| title_sort | bacvc bi adaptive and cross view consistency for incomplete multi view subspace clustering |
| topic | Multi-view subspace clustering Incomplete multi-view data High-order correlation Tensor factorization Contrastive learning |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001164 |
| work_keys_str_mv | AT jiaqiyuzhan bacvcbiadaptiveandcrossviewconsistencyforincompletemultiviewsubspaceclustering AT yueshengzhu bacvcbiadaptiveandcrossviewconsistencyforincompletemultiviewsubspaceclustering AT guiboluo bacvcbiadaptiveandcrossviewconsistencyforincompletemultiviewsubspaceclustering |