A new fixed-point algorithm for independent component analysis

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	A new fixed-point algorithm for independent component analysis
	Shi, ZW ; Tang, HW ; Tang, YY
摘要	A 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.
关键词	Independent Component Analysis Blind Source Separation Fixed-point Algorithm
	2004
语种	英语
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	56 页码:467-473
期刊论文类型	Article
收录类别	SCI
WOS记录号	WOS:000188597300029
引用统计	被引频次：24[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/311026/13903
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.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
推荐引用方式 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.