Feature Concatenation Multi-view Subspace Clustering

The consensus information and complementary information of multi-view data ensure the success of multi-view clustering. Since statistic properties of different views are diverse, even incompatible, few approaches directly implement multi-view clustering based on concatenated features. This paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which utilizes the joint view representation of multi-view data so as to leverage both the consensus and complementary information for clustering. Specifically, multi-view data are firstly concatenated into one matrix, which is used to derive a special coefficient matrix enjoying the low-rank property. Then, $l_{2,1}$-norm is integrated into the objective function to deal with the sample-specific and cluster-specific corruptions of multiple views for benefiting the clustering performance. It is noteworthy that the obtained coefficient matrix is not derived by simply applying the Low-Rank Representation (LRR) to the joint view representation. What's more, a novel algorithm based on the Augmented Lagrangian Multiplier (ALM) is designed to optimize the object function. Finally, the spectral clustering algorithm is applied to an adjacency matrix calculated from the coefficient matrix. Comprehensive experiments on six real world datasets illustrate its superiority over several state-of-the-art approaches for multi-view clustering.