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.