An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms

被引：116

作者：

Langlois, Dominic ^{[1
]}

Chartier, Sylvain ^{[1
]}

Gosselin, Dominique ^{[1
]}

机构：

[1] Univ Ottawa, Ottawa, ON, Canada

来源：

TUTORIALS IN QUANTITATIVE METHODS FOR PSYCHOLOGY | 2010年 / 6卷 / 01期

关键词：

D O I：

10.20982/tqmp.06.1.p031

中图分类号：

学科分类号：

摘要：

This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implementation of two ICA algorithms (FastICA and InfoMax) with the use of the Mathematica software.

引用

页码：31 / 38

页数：8

共 14 条

[1]

Amari S, 1996, ADV NEUR IN, V8, P757

[2]

Bell A. J., 1995, Advances in Neural Information Processing Systems 7, P467

[3] AN INFORMATION MAXIMIZATION APPROACH TO BLIND SEPARATION AND BLIND DECONVOLUTION [J].