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2005
Springer

Two Adaptive Matching Learning Algorithms for Independent Component Analysis

10 years 9 months ago
Two Adaptive Matching Learning Algorithms for Independent Component Analysis
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.
Jinwen Ma, Fei Ge, Dengpan Gao
Added 29 Jun 2010
Updated 29 Jun 2010
Type Conference
Year 2005
Where CIS
Authors Jinwen Ma, Fei Ge, Dengpan Gao
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