Sparsity　preserving　projection　(SPP)　is　a　recently　proposed　unsupervised　linear　dimensionality　reduction　method　for　face　recognition,　which　is　based　on　the　recently-emerged　sparse　representation　theory.　It　aims　to　find　a　low-dimensional　subspace　to　best　preserve　the　global　sparse　reconstructive　relationship　of　the　original　data.　In　this　paper,　we　propose　a　supervised　variation　on　SPP　called　supervised　sparsity　preserving　projection　(SSPP).　The　SSPP　method　explicitly　takes　into　account　the　within-class　weight　as　well　as　between-class　weight　and　assigns　different　weights　to　them,　which　attempts　to　strengthen　the　discriminating　power　and　generalization　ability　of　embedded　data　representation.　The　effectiveness　of　the　proposed　SSPP　method　is　verified　on　two　standard　face　databases　(Yale,　AR).