Traditional　dictionary　learning　algorithms　are　used　for　finding　a　sparse　representation　on　high　dimensional　data　by　transforming　samples　into　a　one-dimensional　(1D)　vector.　This　1D　model　loses　the　inherent　spatial　structure　property　of　data.　An　alternative　solution　is　to　employ　Tensor　Decomposition　for　dictionary　learning　on　their　original　structural　form-a　tensor-by　learning　multiple　dictionaries　along　each　mode　and　the　corresponding　sparse　representation　in　respect　to　the　Kronecker　product　of　these　dictionaries.　To　learn　tensor　dictionaries　along　each　mode,　all　the　existing　methods　update　each　dictionary　iteratively　in　an　alternating　manner.　Because　atoms　from　each　mode　dictionary　jointly　make　contributions　to　the　sparsity　of　tensor,　existing　works　ignore　atoms　correlations　between　different　mode　dictionaries　by　treating　each　mode　dictionary　independently.　In　this　paper,　we　propose　a　joint　multiple　dictionary　learning　method　for　tensor　sparse　coding,　which　explores　atom　correlations　for　sparse　representation　and　updates　multiple　atoms　from　each　mode　dictionary　simultaneously.　In　this　algorithm,　the　Frequent-Pattern　Tree　(FP-tree)　mining　algorithm　is　employed　to　exploit　frequent　atom　patterns　in　the　sparse　representation.　Inspired　by　the　idea　of　K-SVD,　we　develop　a　new　dictionary　update　method　that　jointly　updates　elements　in　each　pattern.　Experimental　results　demonstrate　our　method　outperforms　other　tensor　based　dictionary　learning　algorithms.