Multiview　learning　aims　to　learn　beneficial　patterns　from　heterogeneous　data　sources　and　has　captured　growing　attention　in　recent　years.　Most　of　the　previous　research　studies　focused　on　searching　for　an　effective　feature　embedding　of　downstream　tasks　using　diverse　optimization　algorithms,　however,　very　limited　work　has　been　conducted　to　explore　the　connection　between　multiview　learning　and　deep　neural　networks　of　structure　sharing　hidden　layers.　In　this　article,　we　propose　a　multiview　deep　matrix　factorization　model　to　learn　a　shared　compact　representation　from　multiview　data.　First,　the　proposed　model　constructs　a　multiview　auto-encoder　architecture　with　one　shared　encoder　and　multiple　decoders,　where　each　view　corresponds　to　a　factorization　and　the　shared　encoder　leads　to　a　common　hidden　layer.　Accordingly,　matrix　factorizations　from　multiview　data　share　the　last　hidden　layer　for　a　high-level　semantic　representation.　Second,　the　nonnegativity　constraint　of　the　learned　representation　is　transformed　to　the　projection　operation,　which　can　be　easily　achieved　by　activating　weights　of　the　shared　encoder　network.　Third,　this　network　is　trained　with　a　joint　loss　of　the　reconstruction　error　and　the　compactness　loss.　By　employing　the　clustering　layer,　the　proposed　method　serves　as　an　end-to-end　multiview　clustering　method.　Finally,　comprehensive　experiments　on　nine　real-world　datasets　demonstrate　the　superiority　of　the　proposed　method　against　state-of-the-art　multiview　clustering　methods.