Learned parametric mixture based ICA algorithm

Lei Xu

doi:10.1016/S0925-2312(98)00050-2

1998-11-01

PRE

Lei Xu

C. C. Cheung

Шун-ичи Амари

DOI：10.1016/S0925-2312(98)00050-2

原文链接

原文求助

摘要

En 中文

The learned parametric mixture method is presented for a canonical cost function based ICA model on linear mixture, with several new findings. First, its adaptive algorithm is further refined into a simple concise form. Second, the separation ability of this method is shown to be qualitatively superior to its original model with prefixed nonlinearity. Third, a heuristic way is suggested for selecting the number of densities in a Beamed parametric mixture. Finally, experiments have been conducted to show the success of this method on the sources that can either be sub-Gaussian or super-Gaussian, as well as a combination of both the types. (C) 1998 Elsevier Science B.V. All rights reserved.

Keyword:

independent component analysis

parametric density mixture

learning

information theoretic

maximum likelihood

blind separation

nonlinearity