Expectation-maximization approaches to independent component analysis

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	Expectation-maximization approaches to independent component analysis
	Zhong, MJ ; Tang, HW ; Tang, YY
摘要	Expectation-Maximization (EM) algorithms for independent component analysis are presented in this paper. For super-Gaussian sources, a variational method is employed to develop an EM algorithm in closed form for learning the mixing matrix and inferring the independent components. For sub-Gaussian sources, a symmetrical form of the Pearson mixture model (Neural Comput. 11 (2) (1999) 417-441) is used as the prior, which also enables the development of an EM algorithm in fclosed form for parameter estimation. (C) 2004 Elsevier B.V. All rights reserved.
关键词	independent component analysis overcomplete representations EM algorithm variational method
	2004-10-01
语种	英语
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	61 页码:503-512
期刊论文类型	Article
收录类别	SCI
WOS记录号	WOS:000224511500035
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/14008
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 Neuroinformat, Dalian 116023, Peoples R China 2.Dalian Univ Technol, Inst Computat Biol & Bioinformat, 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	Zhong, MJ,Tang, HW,Tang, YY. Expectation-maximization approaches to independent component analysis[J]. NEUROCOMPUTING,2004,61:503-512.
APA	Zhong, MJ,Tang, HW,&Tang, YY.(2004).Expectation-maximization approaches to independent component analysis.NEUROCOMPUTING,61,503-512.
MLA	Zhong, MJ,et al."Expectation-maximization approaches to independent component analysis".NEUROCOMPUTING 61(2004):503-512.