摘要
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.
Matrix factorization is an effective tool for large-scale data processing and analysis. Nonnegative matrix factorization （NMF） method, which decomposes the nonnegative matrix into two non- negative factor matrices, provides a new way for matrix 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 researches, 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.
基金
supported by the National Natural Science Foundation of China(Grant Nos.60205001 and 60021302).
关键词
特征抽取
NMF
模式识别
计算机
nonnegative data, feature extraction, NMF, intrusiondetection, digital watermarking, EEG signal analysis.
