A new constrained independent component analysis method

被引：108
|
作者：
Huang, De-Shuang [1 ]
Mi, Jian-Xun
机构：
[1] Chinese Acad Sci, Hefei Inst Intelligent Machine, Intelligent Comp Lab, Anhui 230031, Peoples R China
[2] Univ Sci & Technol China, Anhui 230026, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2007年 / 18卷 / 05期
基金：
中国国家自然科学基金;
关键词：
constrained optimization; electroencephalogram (EEG); ICA with reference (ICA-R); independent component analysis (ICA);
D O I：
10.1109/TNN.2007.895910
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Constrained independent component analysis (cICA) is a general framework to incorporate a priori information from problem into the negentropy contrast function as constrained terms to form an augmented Lagrangian function. In this letter, a new improved algorithm for cICA is presented through the investigation of the inequality constraints, in which different closeness measurements are compared. The utility of our proposed algorithm is demonstrated by the experiments with synthetic data and electroencephalogram (EEG) data.
引用
收藏
页码：1532 / 1535
页数：4
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