Subspace clustering refers to the task of clustering a collection of points drawn from a high-dimensional space into a union of multiple subspaces that best fits them. State-of-the-art approaches have been proposed for tackling this clustering problem by using the low-rank or sparse optimization techniques. However, most of the traditional subspace clustering methods are developed for single-view data and are not directly applicable to the multi-view scenario. In this paper, we present a Manifold Regularized Multi-view Subspace Clustering (MRMSC) method to better incorporate the correlated and complementary information from different views. MRMSC yields a unified affinity representation by joint optimization across different views. To respect the data manifold locally, the graph Laplacian is constructed to maintain the intrinsic geometrical structure of each view. In the multi-view integration, a sparsity constraint is imposed to the unified affinity representation in order to better reflect the data relationship from multiple views or features. In experiments, we compared the performance of clustering using MRMSC with the single-view and concatenate-multi-view methods on different datasets. The results showed that better clustering performance can be achieved by fusing the multiple features with a unified affinity representation by MRMSC.