In　this　paper,　we　present　a　theoretical　analysis　on　learning　anchors　for　local　coordinate　coding　(LCC),　which　is　a　method　to　model　functions　for　data　lying　on　non-linear　manifolds.　In　our　analysis　several　local　coding　schemes,　i.e.,　orthogonal　coordinate　coding　(OCC),　local　Gaussian　coding　(LGC),　local　Student　coding　(LSC),　are　theoretically　compared,　in　terms　of　the　upper-bound　locality　error　on　any　high-dimension　data;　this　provides　some　insight　to　understand　the　local　coding　for　classification　tasks.　We　further　give　some　interesting　implications　of　our　results,　such　as　tradeoff　between　locality　and　approximation　ability　in　learning　anchors.