Feature Concatenation Multi-view Subspace Clustering
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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.
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