Clustering　high　dimension　multi-view　data　with　complex　intrinsic　properties　and　nonlinear　manifold　structure　is　a　challenging　task　since　these　data　are　always　embedded　in　low　dimension　manifolds.　Inspired　by　Low　Rank　Representation　(LRR),　some　researchers　extended　classic　LRR　on　Grassmann　manifold　or　Product　Grassmann　manifold　to　represent　data　with　non-linear　metrics.　However,　most　of　these　methods　utilized　convex　nuclear　norm　to　leverage　a　low-rank　structure,　which　was　over-relaxation　of　true　rank　and　would　lead　to　the　results　deviated　from　the　true　underlying　ones.　And,　the　computational　complexity　of　singular　value　decomposition　of　matrix　is　high　for　nuclear　norm　minimization.　In　this　paper,　we　propose　a　new　low　rank　model　for　high-dimension　multi-view　data　clustering　on　Product　Grassmann　Manifold　with　the　matrix　tri-factorization　which　is　used　to　control　the　upper　bound　of　true　rank　of　representation　matrix.　And,　the　original　problem　can　be　transformed　into　the　nuclear　norm　minimization　with　smaller　scale　matrices.　An　effective　solution　and　theoretical　analysis　are　also　provided.　The　experimental　results　show　that　the　proposed　method　obviously　outperforms　other　state-of-the-art　methods　on　several　multi-source　human/crowd　action　video　datasets.