Nonnegative independent component analysis based on minimizing. mutual information technique

被引：48

作者：

Zheng, CH

Huang, DS

Sun, ZL

Lyu, MR

Lok, TM

机构：

[1] Chinese Acad Sci, Inst Intelligent Machines, Intelligent Comp Lab, Hefei 230031, Anhui, Peoples R China

[2] Univ Sci & Technol China, Dept Automat, Hefei, Peoples R China

[3] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China

[4] Chinese Univ Hong Kong, Dept Informat Engn, Shatin, Hong Kong, Peoples R China

来源：

NEUROCOMPUTING | 2006年 / 69卷 / 7-9期

基金：

中国国家自然科学基金;

关键词：

independent component analysis (ICA); nonnegative approach; mutual information; neural network;

D O I：

10.1016/j.neucom.2005.06.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel neural network technique for nonnegative independent component analysis is proposed in this letter. Compared with other algorithms, this method can work efficiently even when the source signals are not well grounded. Moreover, this method is insensitive to the particular underlying distribution of the source data. Experimental results demonstrate the advantages of our approach in achieving satisfactory results regardless of whether the source data are well grounded or not. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：878 / 883

页数：6

共 6 条

[1] Linear and nonlinear ICA based on mutual information - the MISEP method [J].