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12 April 2004 Blind source separation: neural net principles and applications
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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.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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