Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Independent Component Analysis

  • Seungjin Choi
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_305

Synonyms

Blind source separation;  Independent factor analysis

Definition

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, hidden variables) being statistically independent. ICA generalizes a widely-used subspace analysis method 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. Thus, ICA is a density estimation method where a linear model is learnt 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.

Introduction

Linear latent variable model assumes that m-dimensional observed data \({{{\bf x}}}_{t} \in {\mathbb{R}}^{m}\)
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Seungjin Choi
    • 1
  1. 1.Department of Computer SciencePohang University of Science and Technology Korea