摘要
介绍了非负矩阵分解(non-negative matrix factorization,NMF)的基本算法思想和一些改进的NMF算法,并对其在一些重要领域内的应用成果及研究现状进行了系统的概括归纳,最后提出NMF方法存在的问题以及今后研究的趋势和展望.
Non-negative Matrix Factorization （NMF） is a method to obtain a linear representation of data with non- negativity constrains. In this paper, the basic theories and some improved algorithms of NMF are introduced, and some important applications are also summarized. Finally, the open problems and the future research trend are presented.
出处
《武汉工业学院学报》
CAS
2010年第1期109-114,共6页
Journal of Wuhan Polytechnic University
关键词
非负矩阵分解
数据处理
实际应用
non-negative matrix factorization
data processing
practical application
