Robust semi-supervised non-negative matrix factorization for binary subspace learning

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 clustering performance on dataset contaminated we propose weighted constraint noise impose manifold learning into factorization. Moreover, utilize discrete hashing constrain learned subspace, which can from data. Experimental results validate robustness effectiveness RSNMF in face Salt Pepper Contiguous Occlusion.