Blind source separation using Renyi's mutual information

被引：96

作者：

Hild, KE ^{[1
]}

Erdogmus, D ^{[1
]}

Príncipe, J ^{[1
]}

机构：

[1] Univ Florida, Computat NeuroEngn Lab, Dept Elect & Comp Engn, Gainesville, FL 32611 USA

来源：

IEEE SIGNAL PROCESSING LETTERS | 2001年 / 8卷 / 06期

基金：

美国国家科学基金会;

关键词：

blind source separation; component analysis; Givens rotations; mutual information; Renyi's entropy; Renyi's information;

D O I：

10.1109/97.923043

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A blind source separation algorithm is proposed that is based on minimizing Renyi's mutual information by means of nonparametric probability density function (PDF) estimation. The two-stage process consists of spatial whitening and a series of Givens rotations and produces a cost function consisting only of marginal entropies, This formulation avoids the problems of PDF inaccuracy due to truncation of series expansion and the estimation of joint PDFs in high-dimensional spaces given the typical paucity of data, Simulations illustrate the superior efficiency, in terms of data length, of the proposed method compared to fast independent component analysis (FastICA), Comon's minimum mutual information, and Bell and Sejnowski's Infomax.

引用

页码：174 / 176

页数：3

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