A new fixed-point algorithm for independent component analysis
Shi, ZW; Tang, HW; Tang, YY
2004
发表期刊NEUROCOMPUTING
ISSN0925-2312
文章类型Article
卷号56页码:467-473
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
收录类别SCI
语种英语
WOS记录号WOS:000188597300029
引用统计
被引频次:19[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
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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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