Multi-view Latent Subspace Clustering Based on Both Global and Local Structure.

Most existing multi-view clustering methods focus on the global structure or local structure among samples, and few methods focus on the two structures at the same time. In this paper, we propose a Multi-view Latent subspace Clustering based on both Global and Local structure (MLCGL). In this method, a latent embedding representation is learned by exploring the complementary information from different views. In the latent space, not only the global reconstruction relationship but also the local geometric structure among the latent variables are discovered. In this way, a unified affinity graph matrix is constructed in the latent space for different views, which indicates a clear between-class relationship. Meanwhile, a rank constraint is introduced on the Laplacian graph to facilitate the division of samples into the required clusters. In MLCGL, the affinity graph also provides positive feedback to optimize the learned latent representation and contribute to divided it into reasonable clusters. Moreover, we present an alternating iterative optimization scheme to optimize objective functions. Compared with the state-of-art algorithms, MLCGL has achieved excellent experimental performance on several real-world datasets.