A clustering approach for blind source separation with more sources than mixtures

PSYCH OpenIR > 中国科学院心理研究所回溯数据库(1956-2010)

	A clustering approach for blind source separation with more sources than mixtures
	Shi, ZW ; Tang, HW ; Tang, YY ; Yin, FL ; Wang, J ; Guo, CG
摘要	In this paper, blind source separation is discussed with more sources than mixtures when the sources are sparse. The blind separation technique includes two steps. The first step is to estimate a mixing matrix, and the second is to estimate sources. The mixing matrix can be estimated by using a clustering approach which is described by the generalized exponential mixture model. The generalized exponential mixture model is a powerful uniform framework to learn the mixing matrix for sparse sources. After the mixing matrix is estimated, the sources can be obtained by solving a linear programming problem. The techniques we present here can be extended to the blind separation of more sources than mixtures with a Gaussian noise.
	2004
语种	英语
发表期刊	ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1
ISSN	0302-9743
卷号	3173 页码:684-689
期刊论文类型	Article
收录类别	ISTP ; SCI
WOS记录号	WOS:000223492600112
引用统计
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/13905
专题	中国科学院心理研究所回溯数据库(1956-2010)
作者单位	1.Dalian Univ Technol, Inst Computat Biol & Bioinformat, Dalian 116023, Peoples R China 2.Dalian Univ Technol, Inst Neuroinformat, Dalian 116023, Peoples R China 3.Chinese Acad Sci, Lab Visual Informat Proc, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Key Lab Mental Hlth, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Shi, ZW,Tang, HW,Tang, YY,et al. A clustering approach for blind source separation with more sources than mixtures[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1,2004,3173:684-689.
APA	Shi, ZW,Tang, HW,Tang, YY,Yin, FL,Wang, J,&Guo, CG.(2004).A clustering approach for blind source separation with more sources than mixtures.ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1,3173,684-689.
MLA	Shi, ZW,et al."A clustering approach for blind source separation with more sources than mixtures".ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1 3173(2004):684-689.