Redundant　Label　Learning　(RLL)　aims　at　inducing　a　robust　model　from　training　data,　where　each　example　is　associated　with　a　set　of　candidate　labels,　among　which　some　of　them　are　incorrect.　Most　existing　approaches　deal　with　such　problem　by　disambiguating　the　candidate　labels　first　and　then　inducing　the　predictive　model　from　the　disambiguated　data.　However,　these　approaches　only　focus　on　disambiguation　for　each　instance＇　candidate　label　set,　while　the　global　label　context　tends　to　be　ignored.　Meanwhile,　these　approaches　usually　induce　the　objective　model　by　directly　utilizing　the　original　feature　information,　which　may　lead　to　the　model　overfitting　due　to　high-dimensional　redundant　features.　To　tackle　the　above　issues,　we　propose　a　novel　feature　SubspacE　Representation　and　label　Global　DisambiguatIOn　(SERGIO)　approach,　which　improves　the　generalization　ability　of　the　learning　system　from　the　perspective　of　both　feature　space　and　label　space.　Specifically,　we　project　the　original　high-dimensional　feature　space　into　a　low-dimensional　subspace,　where　the　projection　matrix　is　regularized　with　an　orthogonality　constraint　to　make　the　subspace　more　compact.　Meanwhile,　we　introduce　a　label　confidence　matrix　and　constrain　it　with　l(1)-norm　and　trace-norm　regularization　simultaneously,　which　are　utilized　to　explore　global　label　correlations　and　further　well　in　accordance　with　the　nature　of　single-label　classification　and　multi-label　classification　problem,　respectively.　Extensive　experiments　on　both　single-label　and　multi-label　RLL　datasets　demonstrate　that　our　proposed　method　achieves　competitive　performance　against　state-of-the-art　approaches.