Expectation-Maximization approaches to independent component analysis

Mingjun Zhong; Huanwen Tang; Yiyuan Tang

doi:10.1016/j.neucom.2004.06.007

Expectation-Maximization approaches to independent component analysis

Mingjun Zhong, Huanwen Tang, Yiyuan Tang

Psychological Sciences

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	503-512
Number of pages	10
Journal	Neurocomputing
Volume	61
Issue number	1-4
DOIs	https://doi.org/10.1016/j.neucom.2004.06.007
State	Published - Oct 2004

Keywords

EM algorithm
Independent component analysis
Overcomplete representations
Variational method

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10.1016/j.neucom.2004.06.007

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title = "Expectation-Maximization approaches to independent component analysis",

abstract = "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.",

keywords = "EM algorithm, Independent component analysis, Overcomplete representations, Variational method",

author = "Mingjun Zhong and Huanwen Tang and Yiyuan Tang",

note = "Funding Information: We are indebted to Hongjun Chen for his helpful comments on the manuscript. The work was supported by NSFC (30170321, 90103033), MOST (2001CCA00700) and MOE (KP0302). ",

year = "2004",

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AU - Zhong, Mingjun

AU - Tang, Huanwen

AU - Tang, Yiyuan

N1 - Funding Information: We are indebted to Hongjun Chen for his helpful comments on the manuscript. The work was supported by NSFC (30170321, 90103033), MOST (2001CCA00700) and MOE (KP0302).

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Y1 - 2004/10

N2 - 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.

AB - 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.

KW - EM algorithm

KW - Independent component analysis

KW - Overcomplete representations

KW - Variational method

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VL - 61

SP - 503

EP - 512

JO - Neurocomputing

JF - Neurocomputing

IS - 1-4

ER -

Expectation-Maximization approaches to independent component analysis

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Keywords

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