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	基于双线性型的非负矩阵集分解
	李乐 ; 章毓晋 ; LI Le ; ZHANG Yu-Jin
	2010-06-09 ; 2010-06-09
关键词	非负矩阵集分解 双线性型 非负矩阵分解 多元数据描述 图像描述 特征提取 Non-negative Matrix Set Factorization(NMSF) bilinear form Nonnegative Matrix Factorization(NMF) multivariate data representation image representation feature extraction TP391.41
其他题名	Bilinear Form-Based Non-Negative Matrix Set Factorization
中文摘要	非负矩阵分解(Non-negative Matrix Factorization,NMF)是一种常用的非负多元数据描述方法.处理数据矩阵集时,NMF描述力不强、推广性差.为解决这两个问题,并保留NMF的好特性,该文提出了非负矩阵集分解(Non-negative Matrix Set Factorization,NMSF)的概念,并在NMSF的框架下系统研究了基于双线性型的非负矩阵集分解(Bilinear Form-Based Non-negative Matrix Set Factorization,BFBNMSF),构造了单调下降的BFBNMSF算法.理论分析和实验结果均表明:处理数据矩阵集时,BFBNMSF比NMF描述力强、推广性好.由此可认为,此时BFBNMSF比NMF更善于抓住数据的本质特征.; Non-negative Matrix Factorization(NMF)is a popular technique for representations of non-negative multivariate data.While treating a set of matrices,NMF is confronted with two main problems(unsatisfactory accuracy of representation and bad generality).In this paper,Non-negative Matrix Set Factorization(NMSF)is conceived to overcome the two problems and to retain NMF's good properties.Under the frame of NMSF,Bilinear Form-Based Non-negative Matrix Set Factorization(BFBNMSF)is systematically studied,and a monotonic algorithm of BFBNMSF is put forward.Theoretical analysis and experimental results show that while processing a data matrix-set,BFBNMSF results in more accurate representation and holds better generality than NMF,therefore it tends to extract more essential features of data matrix sets than NMF.; 国家自然科学基金(60872084)资助~~
语种	中文 ; 中文
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/53512]
专题	清华大学
推荐引用方式 GB/T 7714	李乐,章毓晋,LI Le,等. 基于双线性型的非负矩阵集分解[J],2010, 2010.
APA	李乐,章毓晋,LI Le,&ZHANG Yu-Jin.(2010).基于双线性型的非负矩阵集分解..
MLA	李乐,et al."基于双线性型的非负矩阵集分解".(2010).

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