Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint. (May 2020)

• Record Type: Journal Article
• Authors: Lu, Gui-Fu; Yu, Qin-Ru; Wang, Yong; Tang, Ganyi
• Is Part Of: Neural networks. Volume 125(2020)
• Page Start: 214
• Page End: 223
• Publication Date: 2020-05
• Subjects: Multi-view features -- Subspace clustering -- Manifold regularization -- Low-rank tensor representation; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.02.014
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, we propose a novel hyper-Laplacian regularized multiview subspace clustering with low-rank tensor constraint method, which is referred as HLR-MSCLRT. In the HLR-MSCLRT model, the subspace representation matrices of different views are stacked as a tensor, and then the high order correlations among data can be captured. To reduce the redundancy information of the learned subspace representations, a low-rank constraint is adopted to the constructed tensor. Since data in the real world often reside in multiple nonlinear subspaces, the HLR-MSCLRT model utilizes the hyper-Laplacian graph regularization to preserve the local geometry structure embedded in a high-dimensional ambient space. An efficient algorithm is also presented to solve the optimization problem of the HLR-MSCLRT model. The experimental results on some data sets show that the proposed HLR-MSCLRT model outperforms many state-of-the-art multi-view clustering approaches.