Spectral　clustering　has　been　an　attractive　topic　in　the　field　of　computer　vision　due　to　the　extensive　growth　of　applications,　such　as　image　segmentation,　clustering　and　representation.　In　this　problem,　the　construction　of　the　similarity　matrix　is　a　vital　element　affecting　clustering　performance.　In　this　paper,　we　propose　a　multi-view　joint　learning　(MVJL)　framework　to　achieve　both　a　reliable　similarity　matrix　and　a　latent　low-dimensional　embedding.　Specifically,　the　similarity　matrix　to　be　learned　is　represented　as　a　convex　hull　of　similarity　matrices　from　different　views,　where　the　nuclear　norm　is　imposed　to　capture　the　principal　information　of　multiple　views　and　improve　robustness　against　noise/outliers.　Moreover,　an　effective　low-dimensional　representation　is　obtained　by　applying　local　embedding　on　the　similarity　matrix,　which　preserves　the　local　intrinsic　structure　of　data　through　dimensionality　reduction.　With　these　techniques,　we　formulate　the　MVJL　as　a　joint　optimization　problem　and　derive　its　mathematical　solution　with　the　alternating　direction　method　of　multipliers　strategy　and　the　proximal　gradient　descent　method.　The　solution,　which　consists　of　a　similarity　matrix　and　a　low-dimensional　representation,　is　ultimately　integrated　with　spectral　clustering　or　K-means　for　multi-view　clustering.　Extensive　experimental　results　on　real-world　datasets　demonstrate　that　MVJL　achieves　superior　clustering　performance　over　other　state-of-the-art　methods.