As　in　single-label　learning,　multi-label　learning　(MLL)　also　suffers　from　the　problem　of　“the　curse　of　dimensionality”　due　to　the　redundancy　of　features　in　the　original　data.　To　address　this　problem,　extensive　research　has　been　conducted　to　explore　the　various　feature　selection　algorithms　aiming　to　eliminate　irrelevant　and　redundant　features.　Among　these,　the　manifold　learning　based　methods　have　gained　significant　attention　for　their　ability　to　capture　local　label　correlations.　However,　previous　approaches　have　mainly　focused　on　extracting　information　from　data　space,　overlooking　valuable　information　in　feature　space.　In　addition,　these　methods　conduct　manifold　learning　and　feature　selection　independently,　thereby　degrading　the　learning　performance.　To　tackle　this　problem,　we　propose　a　novel　multi-label　feature　selection　method　based　on　adaptive　dual-graph　optimization　(MFS-ADGO)　to　learn　the　geometric　structure　of　the　data　and　select　discriminative　feature　subsets　simultaneously.　Specifically,　a　dual-graph　manifold　regularization　is　constructed　first,　to　locally　capture　label　correlations　in　the　data　and　feature　spaces　for　discriminative　features　selection.　A　constraint　is　then　imposed　on　the　similarity　matrices　to　adaptively　learn　the　local　geometric　structure　of　the　data　during　the　feature　selection,　thereby　capturing　more　reliable　information　from　the　original　data.　To　facilitate　feature　selection,　an　objective　function　incorporating　the　l2,1−2-norm　is　proposed,　along　with　an　iterative　updating　algorithm.　Extensive　experiments　unequivocally　demonstrated　the　efficacy　of　MFS-ADGO　in　enhancing　state-of-the-art　feature　selection　methods　in　the　realm　of　MLL.　©　2023　Elsevier　Ltd