Local　learning　has　been　successfully　applied　in　pattern　recognition　due　to　its　powerful　discriminating　ability.　The　conventional　local　learning　usually　divides　the　feature　space　into　a　number　of　homogeneous　local　regions.　By　contrast,　we　introduce　a　new　local　strategy,　which　divides　the　feature　space　into　two　kinds　of　regions:　positive　local　regions　and　negative　local　regions.　Based　on　this　strategy,　a　local　proximal　classifier　(LPC)　is　constructed.　Furthermore,　to　avoid　overfitting,　we　propose　a　local　proximal　classifier　with　global　regularizer　(GLPC)　by　improving　the　LPC　so　that　the　local　classifiers　are　smoothly　glued　together.　In　GLPC,　the　local　correlation　is　modeled　to　capture　the　sample　distribution　among　the　local　region,　resulting　in　increasing　the　discriminating　ability.　Experimental　results　show　the　effectiveness　of　our　classifiers　in　both　classification　accuracy　and　computation　time.　(C)　2014　Elsevier　B.V.　All　rights　reserved.