Abstract Non-negative matrix factorization and its extensions were applied to various areas (i.e., dimensionality reduction, clustering, etc.). When the original data are corrupted by outliers noise, most of non-negative methods cannot achieve robust learn a subspace with binary codes. This paper puts forward semi-supervised method for learning, called RSNMF, image clustering. For better cluste...