Applications of Independent Component Analysis
Identifieur interne : 000211 ( Main/Exploration ); précédent : 000210; suivant : 000212Applications of Independent Component Analysis
Auteurs : Erkki Oja [Finlande]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
Abstract
Abstract: Blind source separation (BSS) is a computational technique for revealing hidden factors that underlie sets of measurements or signals. The most basic statistical approach to BSS is Independent Component Analysis (ICA). It assumes a statistical model whereby the observed multivariate data are assumed to be linear or nonlinear mixtures of some unknown latent variables with nongaussian probability densities. The mixing coefficients are also unknown. By ICA, these latent variables can be found. This article gives the basics of linear ICA and reviews the efficient FastICA algorithm. Then, the paper lists recent applications of BSS and ICA on a variety of problem domains.
Url:
DOI: 10.1007/978-3-540-30499-9_162
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