Dimensionality　reduction　is　an　essential　problem　in　data　mining　and　machine　learning　fields.　Locality　Preserving　Projection　(LPP)　is　a　well-known　dimensionality　reduction　method　which　can　preserve　the　neighborhood　graph　structure　of　data,　and　has　achieved　promising　performance.　However　linear　projection　makes　it　difficult　to　analyze　complex　data　with　nonlinear　structure.　In　order　to　deal　with　this　issue,　this　paper　proposes　a　novel　nonlinear　locality　preserving　projection　method　via　deep　neural　network,　termed　as　DNLPP,　which　replaces　the　linear　projection　with　an　appropriate　deep　neural　network.　Benefiting　from　the　nonlinearity　of　neural　networks　and　its　powerful　representation　capability,　the　proposed　method　is　more　discriminative　than　the　conventional　LPP.　In　order　to　solve　the　new　model,　we　propose　an　iterative　optimization　algorithm.　Extensive　experiments　on　several　public　datasets　illustrate　that　the　proposed　method　is　overall　superior　to　the　other　state-of-art　dimensionality　reduction　methods.　©　2019　IEEE.