Flexible Independent Component Analysis

Seungjin Choi , Andrzej Cichocki , Shun-Ichi Amari

Abstract

This paper addresses an independent component analysis (ICA) learning algorithm with flexible nonlinearity, so named as flexible ICA, that is able to separate instantaneous mixtures of sub- and super-Gaussian source signals. In the framework of natural Riemannian gradient, we employ the parameterized generalized Gaussian density model for hypothesized source distributions. The nonlinear function in the flexible ICA algorithm is controlled by the Gaussian exponent according to the estimated kurtosis of demixing filter output. Computer simulation results and performance comparison with existing methods are presented.
Author Seungjin Choi
Seungjin Choi,,
-
, Andrzej Cichocki (FoEE / ITEEMIS)
Andrzej Cichocki,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, Shun-Ichi Amari
Shun-Ichi Amari,,
-
Journal seriesJournal of VLSI signal processing systems for signal, image and video technology, ISSN 0922-5773, 1573-109X
Issue year2000
Vol26
No1-2
Pages25-38
Keywords in EnglishElectronic and Computer Engineering, Signal, Image and Speech Processing
DOIDOI:10.1023/A:1008135131269
URL http://link.springer.com/article/10.1023/A%3A1008135131269
Score (nominal)0
Citation count*225 (2015-03-25)
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