Low-rank tensor multi-view subspace clustering via cooperative regularization

被引:0
|
作者
Guoqing Liu
Hongwei Ge
Shuzhi Su
Shuangxi Wang
机构
[1] Jiangnan University,School of Artificial Intelligence and Computer Science
[2] Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University),School of Computer Science and Engineering
[3] Ministry of Education,undefined
[4] Anhui University of Science & Technology,undefined
来源
关键词
Hypergraph regularization; Tikhonov regularization; Low-rank; Tensor; Subspace clustering;
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学科分类号
摘要
In order to explore the importance of the hypergraph regularization and the Tikhonov regularization in multi-view clustering, this paper proposes a novel multi-view clustering model, termed as low-rank tensor multi-view subspace clustering via collaborative regularization (LT-MSCCR). The LT-MSCCR model introduces the idea of tensor. The tensor is designed to represent the result of superimposing the subspace representation matrix, which is used to explore the high order correlations between different views, and the low-rank restriction is performed for this tensor to effectively reduce redundant information. Furthermore, we adopt the hypergraph regularization to mine effective geometric information in multi-view data. Meanwhile, we also impose the Tikhonov regularization constraint on the subspace representation matrix so as to improve the smoothness of the subspace representation matrix and enhance the recognition performance of the proposed method. In addition, we also designed a valuable approach to optimizing the proposed model and theoretically analyzing the convergence of the LT-MSCCR method. The experimental results on some datasets show that the proposed model is better than many advanced multi-view clustering methods.
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页码:38141 / 38164
页数:23
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