Chinese Science Bulletin

, Volume 51, Issue 1, pp 7–18

Nonnegative matrix factorization and its applications in pattern recognition

  • Liu Weixiang 
  • Zheng Nanning 
  • You Qubo 
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DOI: 10.1007/s11434-005-1109-6

Cite this article as:
Liu, W., Zheng, N. & You, Q. CHINESE SCI BULL (2006) 51: 7. doi:10.1007/s11434-005-1109-6

Abstract

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 nonnegative 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.

Keywords

nonnegative data feature extraction NMF intrusion detection digital watermarking EEG signal analysis 

Copyright information

© Science in China Press 2006

Authors and Affiliations

  • Liu Weixiang 
    • 1
  • Zheng Nanning 
    • 1
  • You Qubo 
    • 1
  1. 1.Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong UniversityXi’anChina