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Proceedings Paper

Unified parametric ICA algorithm for hybrid sources and its stability analysis
Author(s): Fasong Wang; Hongwei Li; Rui Li; Lihua Fu
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Paper Abstract

Independent component analysis (ICA) refers to extract independent signals from their linear mixtures without assuming prior knowledge of their mixing coefficients. The purpose of this paper is to develop a novel unified parametric ICA algorithm, which enable to separate hybrid source signals including symmetric and asymmetric sources with a self-adaptive score functions. It is derived from the parameterized asymmetric generalized Gaussian density (AGGD) model. The parameters of the score function in the algorithm can be chosen adaptively by estimating the high order statistics of the observed signals online. Stability analysis of the proposed AGGD-ICA learning algorithm is also discussed. Compared with conventional ICA algorithm, the method can separate a wide range of source signals using only one unified density model. Simulations confirm the effectiveness and performance of the proposed algorithm.

Paper Details

Date Published: 4 January 2006
PDF: 5 pages
Proc. SPIE 5985, International Conference on Space Information Technology, 59852E (4 January 2006); doi: 10.1117/12.657660
Show Author Affiliations
Fasong Wang, China Univ. of Geosciences (China)
Hongwei Li, China Univ. of Geosciences (China)
Rui Li, Henan Univ. of Technology (China)
Lihua Fu, China Univ. of Geosciences (China)

Published in SPIE Proceedings Vol. 5985:
International Conference on Space Information Technology
Cheng Wang; Shan Zhong; Xiulin Hu, Editor(s)

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