An algorithm of non-negative matrix factorization with the nearest neighbor after per-treatments

Clustering is a hot topic in machine learning. For high dimension data, nonnegative matrix factorization (NMF) is a crucial technology in clustering. However, NMF has some disadvantages. First, NMF clusters data in original space while outliers and noise will weaken NMF clustering results. Second, NMF does not take local structure which is beneficial for clustering of data into consideration. To address these two disadvantages, a new algorithm is proposed called nonnegative matrix factorization with the nearest neighbor after per-treatments (PNNMF). Per-treatments are used to alleviate effects of outliers and noise. After per-treatments, some credible connected components generated by the nesrest neighbor of data are chosen to capture local structure. Moreover a new initialization for basis matrix is proposed basing these credible connected components. Experiments on real data sets confirm the effectiveness of PNNMF.