With　the　purpose　of　boosting　clustering　performance,　the　manner　of　excavating　underlying　view　correlations　is　an　important　issue　of　multi-view　subspace　clustering　(MSC).　Nevertheless,　regarding　most　MSC　approaches　are　centered　on　the　view　correlations　in　multiple　subspace　representations　and　neglect　the　view　correlations　in　multiple　feature　representations.　To　address　this　limitation,　this　paper　introduces　a　method,　dubbed　Multi-view　Subspace　Clustering　with　View　Correlations　(MSCVC),　which　excavates　underlying　view　correlations　in　both　multiple　feature　representations　and　multiple　subspace　representations　simultaneously.　Specifically,　the　Multi-view　Principal　Component　Analysis　(MPCA)　is　designed　to　capture　the　view　correlations　contained　in　multiple　feature　representations　by　using　the　orthogonal　mapping　and　low-rank　tensor　constraint.　As　a　consequence,　view　correlations　in　multiple　feature　representations　can　be　effectively　investigated　and　simultaneously　embedded　in　the　learned　new　feature　representations.　For　view　correlations　in　multiple　subspace　representations,　the　new　feature　representations　learned　in　MPCA　are　employed　and　the　low-rank　tensor　constraint　is　used　as　well.　Experiments　are　conducted　on　nine　benchmark　datasets　to　demonstrate　the　progressiveness　of　our　MSCVC　compared　to　several　state-of-the-art　algorithms.