Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 473-477.

• Big Data & Data Mining • Previous Articles     Next Articles

Online Learning Nonnegative Matrix Factorization

HE Xiao-wen, HU Yi-fei, WANG Hai-ping, CHEN Mo   

  1. School of Automation,Guangdong University of Technology,Guangzhou 510006,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: This paper proposed a new nonnegative matrix factorization of online form,namely online learning nonnegative matrix factorization(OLNMF).The OLNMF algorithm uses incremental forms of non-smooth model,and adopts “anmesic average method” to control the weight of new and old samples,improving the computational efficiency and reducing the computational complex.OLNMF algorithm can deal with large real-time update data sets,and extract more sparse base matrix.Compared with INMF,ONMFO,Lp-INMF,experiments on face databases show that the proposed method achieves better sparsity,andSVM classification method base on OLNMF achieves better classification accuracy on EEG database.

Key words: Feature extraction, Nonnegative matrix factorization, Online learning, Sparseness constraints

CLC Number: 

  • TP391
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