DocumentCode :
2737736
Title :
An adaptive on-line algorithm for independent component analysis
Author :
Li, Xiao-ou ; Zhou, Yun ; Feng, Huan-qing
Author_Institution :
Inst. of Biomed. Eng., China Univ. of Sci. & Technol. of China, Hefei, China
Volume :
2
fYear :
2003
fDate :
14-17 Dec. 2003
Firstpage :
1338
Abstract :
This paper presents an adaptive on-line algorithm whose optimization criterion is based on maximum likelihood to perform Independent Component Analysis (ICA). A fast density estimation method is introduced , it can quickly achieve the true score functions of the unknown sources by estimating from the sample. In the meantime, the careful selection of the step size is often necessary to obtain good performance for the source separation tasks. We carry out the global minimum of the contrast function with the gradient adaptive step size. The results of simulation experiment show that the provided algorithm can perform the adaptive separation of real digital signal efficiently.
Keywords :
adaptive estimation; blind source separation; digital signals; independent component analysis; maximum likelihood estimation; optimisation; adaptive online algorithm; adaptive separation; contrast function; fast density estimation method; gradient adaptive step size; independent component analysis; maximum likelihood estimation; optimization criterion; real digital signal; source separation tasks; true score functions; Biomedical engineering; Blind source separation; Convergence; Independent component analysis; Mathematical model; Mathematics; Maximum likelihood estimation; Mutual information; Source separation; Statistical analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
0-7803-7702-8
Type :
conf
DOI :
10.1109/ICNNSP.2003.1281119
Filename :
1281119
Link To Document :
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