Restricted　Boltzmann　Machine　(RBM)　is　an　important　generative　model　modeling　vectorial　data.　While　applying　an　RBM　in　practice　to　images,　the　data　have　to　be　vectorized.　This　results　in　high-dimensional　data　and　valuable　spatial　information　has　got　lost　in　vectorization.　In　this　paper,　a　Matrix-Variate　Restricted　Boltzmann　Machine　(MVRBM)　model　is　proposed　by　generalizing　the　classic　RBM　to　explicitly　model　matrix　data.　In　the　new　RBM　model,　both　input　and　hidden　variables　are　in　matrix　forms　which　are　connected　by　bilinear　transforms.　The　MVRBM　has　much　less　model　parameters　while　retaining　comparable　performance　as　the　classic　RBM.　The　advantages　of　the　MVRBM　have　been　demonstrated　on　three　real-world　applications:　handwritten　digit　denoising,　reconstruction　and　recognition.　©　2016　IEEE.