Qinghai Zheng

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  • Feature Concatenation Multi-view Subspace Clustering

    Many multi-view clustering methods have been proposed with the popularity of multi-view data in variant applications. The consensus information and complementary information of multi-view data ensure the success of multi-view clustering. Most of existing methods process multiple views separately by exploring either consensus information or complementary information, and few methods cluster multi-view data based on concatenated features directly since statistic properties of different views are diverse, even incompatible. This paper proposes a novel multi-view subspace clustering method dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which uses the joint view representation of multi-view data to obtain the clustering performance straightforward and leverage both the consensus information and complementary information. Specifically, multiple views are concatenated firstly, then a special coefficient matrix, enjoying the low-rank property, is derived and the spectral clustering algorithm is applied to an affinity matrix calculated from the coefficient matrix. It is notable that the coefficient matrix obtained during clustering process is not derived by applying Low-Rank Representation (LRR) to the joint view representation simply. Furthermore, l_2,1-norm and sparse constraints are introduced to deal with the sample-specific and cluster-specific corruptions of multiple views for benefitting the clustering performance. A novel algorithm based on the Augmented Lagrangian Multiplier (ALM) is designed to optimize the proposed method. Comprehensive experiments compared with several effective multi-view clustering methods on six real-world datasets show the superiority of the proposed work.

    01/30/2019 ∙ by Qinghai Zheng, et al. ∙ 14 share

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  • Constrained Bilinear Factorization Multi-view Subspace Clustering

    Multi-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed and achieved success in real-world applications, most of which assume that all views share a same coefficient matrix. However, the underlying information of multiview data are not exploited effectively under this assumption, since the coefficient matrices of different views should have the same clustering properties rather than be the same among multiple views. To this end, a novel Constrained Bilinear Factorization Multi-view Subspace Clustering (CBF-MSC) method is proposed in this paper. Specifically, the bilinear factorization with an orthonormality constraint and a low-rank constraint is employed for all coefficient matrices to make all coefficient matrices have the same trace-norm instead of being equivalent, so as to explore the consensus information of multi-view data more effectively. Finally, an algorithm based on the Augmented Lagrangian Multiplier (ALM) scheme with alternating direction minimization is designed to optimize the objective function. Comprehensive experiments tested on six benchmark datasets validate the effectiveness and competitiveness of the proposed approach compared with several state-of-the-art approaches.

    06/19/2019 ∙ by Qinghai Zheng, et al. ∙ 0 share

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