As a special class of non-negative matrix factorization, symmetric factorization (SymNMF) has been widely used in the machine learning field to mine hidden non-linear structure data. Due constraint and non-convexity SymNMF, efficiency existing methods is generally unsatisfactory. To tackle this issue, we propose two-phase algorithm solve SymNMF problem efficiently. In first phase, drop new mode...

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...

Properties of a novel algorithm called non-negative matrix factorization (NMF), are studied. NMF can discover substructures and can provide estimations about the presence or the absence of those, being attractive for completion of missing information. We have studied the working and learning capabilities of NMF networks. Performance was improved by adding sparse code shrinkage (SCS) algorithm t...