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Expectation-maximization approaches to independent component analysis
Zhong, MJ; Tang, HW; Tang, YY
2004-10-01
Source PublicationNEUROCOMPUTING
ISSN0925-2312
SubtypeArticle
Volume61Pages:503-512
AbstractExpectation-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.
Keywordindependent component analysis overcomplete representations EM algorithm variational method
Indexed BySCI
Language英语
WOS IDWOS:000224511500035
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/14008
Collection中国科学院心理研究所回溯数据库(1956-2010)
Affiliation1.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
Recommended Citation
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.
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