The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Low-Rank Tensor Constrained Multiview Subspace Clustering
Abstract
In this paper, we explore the problem of multiview subspace clustering. We introduce a low-rank tensor constraint to explore the complementary information from multiple views and, accordingly, establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering (LT-MSC). Our method regards the subspace representation matrices of different views as a tensor, which captures dexterously the high order correlations underlying multiview data. Then the tensor is equipped with a low-rank constraint, which models elegantly the cross information among different views, reduces effectually the redundancy of the learned subspace representations, and improves the accuracy of clustering as well. The inference process of the affinity matrix for clustering is formulated as a tensor nuclear norm minimization problem, constrained with an additional L2,1-norm regularizer and some linear equalities. The minimization problem is convex and thus can be solved efficiently by an Augmented Lagrangian Alternating Direction Minimization (AL-ADM) method. Extensive experimental results on four benchmark datasets show the effectiveness of our proposed LT-MSC method.
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Cites
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Robust Recovery of Subspace Structures by Low-Rank Representation
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Self-citationSparse Subspace Clustering: Algorithm, Theory, and Applications
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Zhan, K., Zhang, C., Guan, J., & Wang, J. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2751646 · Cited by 543
Self-citationFast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance
Wang, S., Liu, X., Zhu, X., Zhang, P., Zhang, Y., Gao, F., & Zhu, E. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3131941 · Cited by 517
Consistent and Specific Multi-View Subspace Clustering
Luo, S., Zhang, C., Zhang, W., & Cao, X. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11617 · Cited by 426
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Metadata
- Authors
- Zhang, C., Fu, H., Liu, S., Liu, G., & Cao, X.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 1582-1590
- OpenAlex
- W2197707282
- Cited by
- 566
- References
- 35
- FWCI
- 13.45
- Type
- conference paper
- Language
- en
- Open access
- closed
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