An Independent Component Analysis Algorithm through Solving Gradient Equation Combined with Kernel Density Estimation

A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation. An iterative method is introduced to solve this equation efficiently. The unknown probability density functions...

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Bibliographic Details
Published inShanghai jiao tong da xue xue bao Vol. 14; no. 2; pp. 204 - 209
Main Author 薛云峰 王宇嘉 杨杰
Format Journal Article
LanguageEnglish
Published Heidelberg Shanghai Jiaotong University Press 01.04.2009
Subjects
Architecture
Computer Science
Electrical Engineering
Engineering
Life Sciences
Materials Science
方程求解
核密度估计
梯度方程
概率密度函数
独立成分分析
算法
自然场景
迭代方法
gradient method
independent component analysis
kernel density estimation
TN 911.7
blind source separation
Online AccessGet full text
ISSN1007-1172
1995-8188
DOI10.1007/s12204-009-0204-2

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Summary:A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation. An iterative method is introduced to solve this equation efficiently. The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method. Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.
Bibliography:31-1943/U
independent component analysis, blind source separation, gradient method, kernel density estimation
TP391.41
O211.67
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-009-0204-2