Nonnegative　Matrix　Factorization　(NMF)　has　been　widely　used　in　dimensionality　reduction,　machine　learning,　and　data　mining,　etc.　It　aims　to　find　two　nonnegative　matrices　whose　product　can　well　approximate　the　nonnegative　data　matrix,　which　naturally　lead　to　parts-based　representation.　In　this　paper,　we　present　a　family　of　projective　nonnegative　matrix　factorization　algorithm,　PNMF　with　Bregman　divergence.　Several　versions　of　divergence　such　as　Euclidean　distance　and　Kullback-Leibler　(KL)　divergence　with　PNMF　have　been　studied.　In　this　paper,　we　investigate　the　MU　rules　to　solve　the　PNMF　with　some　other　divergence,　such　as　β-divergence,　IS-divergence.　It　has　been　shown　that　the　base　matrix　by　Bregman　PNMF　is　better　suitable　for　orthoganal,　localized　and　sparse　representation　than　by　traditional　NMF.　©　2010　IEEE.