Dictionary　in　Local　Coordinate　Coding　(LCC)　is　important　to　approximate　a　non-linear　function　with　linear　ones.　Optimizing　dictionary　from　predefined　coding　schemes　is　a　challenge　task.　This　paper　focuses　on　learning　dictionary　from　two　Locality　Coding　Adaptors　(LCAs),　i.e.,　locality　Gaussian　Adaptor　(GA)　and　locality　Euclidean　Adaptor　(EA),　for　large-scale　and　high-dimension　datasets.　Online　dictionary　learning　is　formulated　as　two　cycling　steps,　local　coding　and　dictionary　updating.　Both　stages　scale　up　gracefully　to　large-scale　datasets　with　millions　of　data.　The　experiments　on　different　applications　demonstrate　that　our　method　leads　to　a　faster　dictionary　learning　than　the　classical　ones　or　the　state-of-the-art　methods.　(C)　2015　Elsevier　B.V.　All　rights　reserved.