Paper Title:
An ICA Algorithm Based on Symmetric and Asymmetric Generalized Gaussian Model
  Abstract

Maximum likelihood estimation is a very popular method to estimate the independent component analysis model because of good performance. Independent component analysis algorithm (the natural gradient method) based on this method is widely used in the field of blind signal separation. It potentially assumes that the source signal was symmetrical distribution, in fact in practical applications, source signals may be asymmetric. This article by distinguishing that the source signal is symmetrical or asymmetrical, proposes an improved natural gradient method based on symmetric generalized Gaussian model (People usually call generalized Gaussian model) and asymmetric generalized Gaussian model. The random mixed-signal simulation results show that the improved algorithm is better than the natural gradient separation method.

  Info
Periodical
Advanced Materials Research (Volumes 204-210)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
470-475
DOI
10.4028/www.scientific.net/AMR.204-210.470
Citation
F. Zhao, Y. J. Zhang, M. Cai, "An ICA Algorithm Based on Symmetric and Asymmetric Generalized Gaussian Model", Advanced Materials Research, Vols. 204-210, pp. 470-475, 2011
Online since
February 2011
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