In this paper, we propose a novel Latent Multi-viewSubspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and simultaneously performs data reconstruction based on the learned latent representation. With thecomplementarity of multiple views, the latent representationcould depict data themselves more comprehensively thaneach single view individually, accordingly makes subspacerepresentation more accurate and robust as well. The proposed method is intuitive and can be optimized efficiently byusing the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) algorithm. Extensive experiments on benchmark datasets have validated theeffectiveness of our proposed method