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	Two adaptive matching learning algorithms for independent component analysis
	Ma, JW ; Ge, F ; Cao, DP
	2005
关键词	CONJECTURE SEPARATION
英文摘要	Independent component analysis (ICA) has been applied in many fields of signal processing and many ICA learning algorithms have been proposed from different perspectives. However, there is still a lack of a deep mathematical theory to describe the ICA learning algorithm or problem, especially in the cases of both super- and sub-Gaussian sources. In this paper, from the point of view of the one-bit-matching principle, we propose two adaptive matching learning algorithms for the general ICA problem. It is shown by the simulation experiments that the adaptive matching learning algorithms can efficiently solve the ICA problem with both super- and sub-Gaussian sources and outperform the typical existing ICA algorithms in certain aspects.; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; SCI(E); CPCI-S(ISTP); 0
语种	英语
出处	SCI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/315009]
专题	数学科学学院
推荐引用方式 GB/T 7714	Ma, JW,Ge, F,Cao, DP. Two adaptive matching learning algorithms for independent component analysis. 2005-01-01.

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