Expectation-maximization approaches to independent component analysis
Zhong, MJ; Tang, HW; Tang, YY
2004-10-01
发表期刊NEUROCOMPUTING
ISSN0925-2312
文章类型Article
卷号61页码:503-512
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
收录类别SCI
语种英语
WOS记录号WOS:000224511500035
引用统计
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
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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.
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