New Gaussianity measures based on order statistics: application to ICA

被引：5
|
作者：
Blanco, Y [1 ]
Zazo, S [1 ]
机构：
[1] Univ Politecn Madrid, ETS Ingn Telecommun, E-28040 Madrid, Spain
来源：
NEUROCOMPUTING | 2003年 / 51卷
关键词：
independent component analysis; blind source separation; cumulative density function; order statistics; Gaussianity measure;
D O I：
10.1016/S0925-2312(01)00707-X
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we propose and analyze a set of alternative statistical distances between distributions based on the cumulative density function instead of traditional probability density function. In particular, these new Gaussian distances provide new cost functions whose maximization performs the extraction of one independent component at each successive stage of a deflation ICA procedure. The new Gaussianity measures improve the ICA performance and also increase the robustness against outliers in comparison with traditional ones. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：303 / 320
页数：18
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