Graph-based　Multi-View　Clustering　(GMVC)　has　received　extensive　attention　due　to　its　ability　to　capture　the　neighborhood　relationship　among　data　points　from　diverse　views.　However,　most　existing　approaches　construct　similarity　graphs　from　the　original　multi-view　data,　the　accuracy　of　which　heavily　and　implicitly　relies　on　the　quality　of　the　original　multiple　features.　Moreover,　previous　methods　either　focus　on　mining　the　multi-view　commonality　or　emphasize　on　exploring　the　multi-view　individuality,　making　the　rich　information　contained　in　multiple　features　cannot　be　effectively　exploited.　In　this　work,　we　design　a　novel　GMVC　framework　via　cOmmoNality　and　Individuality　discOvering　in　lateNt　subspace　(ONION),　seeking　for　a　robust　and　discriminative　subspace　representation　compatible　across　multiple　features　for　GMVC.　To　be　specific,　our　method　simultaneously　formulates　the　unsupervised　sparse　feature　selection　and　the　robust　subspace　extraction,　as　well　as　the　target　graph　learning　in　a　unified　optimization　model,　which　can　help　the　learning　of　the　discriminative　subspace　representation　and　the　target　graph　in　a　mutual　reinforcement　manner.　Meanwhile,　we　manipulate　the　target　graph　by　an　explicit　structural　penalty,　rendering　the　connected　components　in　the　graph　directly　reveal　clusters.　Experimental　results　on　seven　benchmark　datasets　demonstrate　the　effectiveness　of　our　proposed　method.