Abstract Inspired by low rank representation and sparse subspace clustering acquiring success, ones attempt to simultaneously perform low rank and sparse constraints on the affinity matrix to improve the performance. However, it is just a trade-off between these two constraints. In this paper, we propose a novel Cascaded Low Rank and Sparse Representation (CLRSR) method for subspace clustering, which seeks the sparse expression on the former learned low rank latent representation. By this cascaded way, the sparse and low rank properties of the data are revealed adequately. Additionally, we extent CLRSR onto Grassmann manifolds to deal with multi-dimension data such as imageset or videos. An effective solution and its convergence analysis are also provided. The experimental results demonstrate the proposed method has excellent performance compared with state-of-the-art clustering methods.