隐式低秩稀疏表示的多视角子空间聚类

To solve the problem of multi-view clustering, a latent low-rank sparse multi-view subspace clustering(LLSMSC) algorithm is proposed. A latent space shared by all views is constructed to explore the complementary information of multi-view data. The global and local structure of multi-view data can be captured to attain promising clustering results by imposing low-rank constraint and sparse constraint on the implicit latent subspace representation simultaneously. An algorithm based on augmented Lagrangian multiplier with alternating direction minimization strategy is employed to solve the optimization problem. Experiments on six benchmark datasets verify the effectiveness and superiority of LLSMSC.