Blind source separation: neural net principles and applications

Erkki Oja

doi:10.1117/12.548912

12 April 2004 Blind source separation: neural net principles and applications

Erkki Oja

Proceedings Volume 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II; (2004) https://doi.org/10.1117/12.548912
Event: Defense and Security, 2004, Orlando, Florida, United States

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 relates the problem and the solution algorithms to neural learning rules, which can be seen as extensions of some classical Principal Component Analysis learning rules. Also the more efficient FastICA algorithm is briefly reviewed. Finally, the paper lists recent applications of BSS and ICA on a variety of problem domains.

Citation Download Citation

Erkki Oja "Blind source separation: neural net principles and applications", Proc. SPIE 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II, (12 April 2004); https://doi.org/10.1117/12.548912

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