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A new fixed-point algorithm for independent component analysis
Shi, ZW; Tang, HW; Tang, YY
2004
Source PublicationNEUROCOMPUTING
ISSN0925-2312
SubtypeArticle
Volume56Pages:467-473
AbstractA new fixed-point algorithm for independent component analysis (ICA) is presented that is able blindly to separate mixed signals with sub- and super-Gaussian source distributions. The new fixed-point algorithm maximizes the likelihood of the ICA model under the constraint of decorrelation and uses the method of Lee et al. (Neural Comput. 11(2) (1999) 417) to switch between sub- and super-Gaussian regimes. The new fixed-point algorithm maximizes the likelihood very fast and reliably. The validity of this algorithm is confirmed by the simulations and experimental results. (C) 2003 Elsevier B.V. All rights reserved.
KeywordIndependent Component Analysis Blind Source Separation Fixed-point Algorithm
Indexed BySCI
Language英语
WOS IDWOS:000188597300029
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/13903
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.Chinese Acad Sci, Lab Visual Informat Proc, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Key Lab Mental Hlth, Beijing 100101, Peoples R China
4.Dalian Univ Technol, Inst Computat Biol & Bioinformat, Dalian 116023, Peoples R China
Recommended Citation
GB/T 7714
Shi, ZW,Tang, HW,Tang, YY. A new fixed-point algorithm for independent component analysis[J]. NEUROCOMPUTING,2004,56:467-473.
APA Shi, ZW,Tang, HW,&Tang, YY.(2004).A new fixed-point algorithm for independent component analysis.NEUROCOMPUTING,56,467-473.
MLA Shi, ZW,et al."A new fixed-point algorithm for independent component analysis".NEUROCOMPUTING 56(2004):467-473.
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