Nonnegative matrix factorization and its applications in pattern recognition

Matrix factorization has been widely used in many fields1]. In numerical algebra, the large-scale and com- plex problems can be transformed into small-scale sim- ple subproblems by matrix factorization; in applied statistics, low rank approximation of or…

Nonnegative matrix factorization and its applications in pattern recognition

Matrix factorization is an effective tool for large-scale data processing and analysis. Non- negative matrix factorization (NMF) method, which decomposes the nonnegative matrix into two non- negative factor matrices, provides a new way for ma- trix factorization. NMF is significant in intelligent information processing and pattern recognition. This paper firstly introduces the basic idea of NMF and some new relevant methods. Then we discuss the loss functions and relevant algorithms of NMF in the framework of probabilistic models based on our re- searches, and the relationship between NMF and information processing of perceptual process. Finally, we make use of NMF to deal with some practical questions of pattern recognition and point out some open problems for NMF.