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2012 | OriginalPaper | Chapter

Independent Component Analysis

Author : Seungjin Choi

Published in: Handbook of Natural Computing

Publisher: Springer Berlin Heidelberg

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Abstract

Independent component analysis (ICA) is a statistical method, the goal of which is to decompose multivariate data into a linear sum of non-orthogonal basis vectors with coefficients (encoding variables, latent variables, and hidden variables) being statistically independent. ICA generalizes widely used subspace analysis methods such as principal component analysis (PCA) and factor analysis, allowing latent variables to be non-Gaussian and basis vectors to be non-orthogonal in general. ICA is a density-estimation method where a linear model is learned such that the probability distribution of the observed data is best captured, while factor analysis aims at best modeling the covariance structure of the observed data. We begin with a fundamental theory and present various principles and algorithms for ICA.

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Metadata
Title
Independent Component Analysis
Author
Seungjin Choi
Copyright Year
2012
Publisher
Springer Berlin Heidelberg
Book
Handbook of Natural Computing

Print ISBN: 978-3-540-92909-3

Electronic ISBN: 978-3-540-92910-9

Copyright Year: 2012

DOI
https://doi.org/10.1007/978-3-540-92910-9_13

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