Topic　models,　such　as　probabilistic　latent　semantic　analysis　(PLSA)　and　latent　Dirichlet　allocation　(LDA),　have　shown　impressive　success　in　many　fields.　Recently,　multi-view　learning　via　probabilistic　latent　semantic　analysis　(MVPLSA),　is　also　designed　for　multi-view　topic　modeling.　These　approaches　are　instances　of　generative　model,　whereas　they　all　ignore　the　manifold　structure　of　data　distribution,　which　is　generally　useful　for　preserving　the　nonlinear　information.　In　this　paper,　we　propose　a　novel　multiple　graph　regularized　generative　model　to　exploit　the　manifold　structure　in　multiple　views.　Specifically,　we　construct　a　nearest　neighbor　graph　for　each　view　to　encode　its　corresponding　manifold　information.　A　multiple　graph　ensemble　regularization　framework　is　proposed　to　learn　the　optimal　intrinsic　manifold.　Then,　the　manifold　regularization　term　is　incorporated　into　a　multi-view　topic　model,　resulting　in　a　unified　objective　function.　The　solutions　are　derived　based　on　the　Expectation　Maximization　optimization　framework.　Experimental　results　on　real-world　multi-view　data　sets　demonstrate　the　effectiveness　of　our　approach.　(C)　2017　Elsevier　B.V.　All　rights　reserved.