  Recent years have witnessed a growing academic interest in multi-view
subspace clustering. In this paper, we propose a novel Double Graphs
Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to
harness both global and local structural information of multi-view data in a
unified framework. Specifically, DGRMSC firstly learns a latent representation
to exploit the global complementary information of multiple views. Based on the
learned latent representation, we learn a self-representation to explore its
global cluster structure. Further, Double Graphs Regularization (DGR) is
performed on both latent representation and self-representation to take
advantage of their local manifold structures simultaneously. Then, we design an
iterative algorithm to solve the optimization problem effectively. Extensive
experimental results on real-world datasets demonstrate the effectiveness of
the proposed method.
