Multi-view learning models the relationships between various observations, and is adept to explore the underlying information of data from multiple perspectives. Since well representation is vital for self-expressive subspace clustering, we propose a method called Multi-View Subspace Clustering with Consistent and view-Specific Latent Factors and Coefficient Matrices (MVSC-CSLFCM) that explores the consensus and complementary information of multiple views, and we also impose suitable constraints on coefficient matrices corresponding to the obtained view-specific and consistent representations, respectively. Finally, an effective optimization algorithm based on augmented lagrangian multiplier is introduced to optimize our proposed MVSC-CSLFCM. Comprehensive experiments on four real-world data sets demonstrate the superiority of our proposed method by comparing with a series of state-of-art subspace algorithms.