The　low-rank　representation　(LRR)　method　has　attracted　widespread　attention　due　to　its　excellent　performance　in　pattern　recognition　and　machine　learning.　LRR-based　variants　have　been　proposed　to　solve　the　three　existing　problems　in　LRR:　(1)　the　projection　matrix　is　permanently　fixed　when　dimensionality　reduction　techniques　are　adopted;　(2)　LRR　fails　to　capture　the　local　geometric　structure;　and　(3)　the　solution　deviates　from　the　real　low-rank　solution.　To　address　these　problems,　this　paper　proposes　a　low-rank　representation　with　projection　distance　regularization　(PDRLRR)　via　manifold　optimization　for　clustering.　In　detail,　we　first　introduce　a　low-dimensional　projection　matrix　and　a　projection　distance　regularization　term　to　fit　the　projected　data　automatically　and　capture　the　local　structure　of　the　data,　respectively.　Consequently,　the　projection　matrix　and　representation　matrix　are　obtained　jointly.　Then,　we　obtain　a　more　accurate　low-rank　solution　by　minimizing　the　Schatten-p　norm　instead　of　the　nuclear　norm.　Next,　the　projection　matrix　is　optimized　through　a　generalized　Stiefel　manifold.　Extensive　experiments　demonstrate　that　our　proposed　method　outperforms　the　state-of-the-art　methods.