Independent component analysis: algorithms and applications

A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the origina...

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Bibliographic Details
Published in:Neural networks Vol. 13; no. 4; pp. 411 - 430
Main Authors: Hyvärinen, A., Oja, E.
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.05.2000
Elsevier Science
Subjects:
Algorithms
Applied sciences
Blind signal separation
Brain - physiology
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Factor analysis
Humans
Independent component analysis
Magnetoencephalography
Neural networks
Neural Networks (Computer)
Normal Distribution
Projection pursuit
Representation
Source separation
Source separation
Representation
Factor analysis
Projection pursuit
Blind signal separation
Independent component analysis
Statistical analysis
Probabilistic approach
Theoretical study
Random vector
Pattern recognition
Neural network
Basis function
Linear combination
Linear transformation
Algorithm analysis
Principal component analysis
ISSN:0893-6080, 1879-2782
Online Access:Get full text
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Summary:A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject.
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ISSN:0893-6080
1879-2782
DOI:10.1016/S0893-6080(00)00026-5