In　the　past　few　decades,　the　clustering　problem　has　made　considerable　progress,　and　co-clustering　algorithms　have　attracted　more　attention.　Compared　with　one-side　clustering,　co-clustering　not　only　groups　samples　according　to　the　distribution　of　features　but　also　groups　features　according　to　the　distribution　of　samples　at　the　same　time.　This　duality　helps　to　explore　the　structural　information　of　data,　such　as　genes　and　texts.　In　this　paper,　a　new　co-clustering　algorithm　is　proposed　to　simultaneously　consider　feature　weights,　data　noise,　local　manifolds,　and　global　scatter,　named　robust　weighted　co-clustering　with　global　and　local　discrimination.　Furthermore,　an　alternate　update　rule　is　put　forward　to　optimize　objective,　theoretically　proven　to　converge.　Then,　the　algorithm＇s　duality,　robustness,　and　effectiveness　have　been　verified　on　synthetic,　corrupted,　and　real　datasets,　respectively.　The　runtime　and　parameter　sensitivity　of　the　algorithm　are　also　analyzed.　Finally,　sufficient　experiments　clarify　the　competitiveness　of　our　algorithm　compared　to　other　ones.　©　2023