Abstract
Low-rank representation has been widely used in multi-view clustering. But the existing methods are matrix-based, which cannot well capture high-order low-rank correlation embedded in multiple views and fail to retain the local geometric structure of features resided in multiple nonlinear subspaces simultaneously. To handle this problem, we propose a nonconvex tensor hypergraph learning for multi-view subspace clustering. In this model, the hyper-Laplacian regularization is used to capture high-order global and local geometric information of all views. The nonconvex weighted tensor Schatten-p norm can better characterize the high-order correlations of multi-view data. In addition, we design an effective alternating direction algorithm to optimize this nonconvex model. Extensive experiments on five datasets prove the robustness and superiority of the proposed method.
This work is supported in part by the National Nature Science Foundation of China (62072312, 61972264), in part by Shenzhen Basis Research Project (JCYJ20210324094009026, JCYJ20200109105832261).
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Notes
- 1.
http://www.uk.research.att.com/facedatabase.html.
- 2.
http://mlg.ucd.ie/datasets/segment.html.
- 3.
http://mlg.ucd.ie/datasets/segment.html.
- 4.
http://www-cvr.ai.uiuc.edu/ponce\(_{-}\)grp/data/.
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Yao, X., Li, M. (2024). Nonconvex Tensor Hypergraph Learning for Multi-view Subspace Clustering. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_4
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