Low-Rank Representation (LRR) is an effective self-expressiveness method, which uses the observed data itself as the dictionary to reconstruct the original data. LRR focuses on representing the global low-dimensional information, but ignores the real fact that data often resides on low-dimensional manifolds embedded in a high-dimensional data. Therefore, LRR can not capture the non-linear geometric structures within data. As well known, locality preserving projections (LPP) is able to preserve the intrinsic geometry structure embedded in high-dimensional data. To this end, we treat the projected data by LPP as an adaptive dictionary, and such a dictionary can capture the intrinsic geometry structures of data. In this way, our method is in favor of the global low-rank representation. Speci cally, the proposed method provides a way to reconstruct the original data from two views, and hence we call this proposed method as Simultaneous Dual-Views Reconstruction with Adaptive Dictionary and Low-Rank Representation. The proposed method can be used for unsupervised feature extraction and subspace clustering. Experiments on benchmark databases show the excellent performance of this proposed method in comparison with other state-of-the-art methods.