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28 March 2005 Blind signal separation: mathematical foundations of ICA, sparse component analysis, and other techniques
Shun-ichi Amari
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Proceedings Volume 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III; (2005) https://doi.org/10.1117/12.607004
Event: Defense and Security, 2005, Orlando, Florida, United States
Abstract
The present paper shows mathematical foundations of ICA (independent component analysis) and related subjects of signal representations. Information geometry plays a basic role for elucidating the structure of the problem underlying signal representation and decomposition. The method of estimating function is used for the analysis of errors and stability for various ICA algorithms. The nonholonomic method is of particularly interest.
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Shun-ichi Amari "Blind signal separation: mathematical foundations of ICA, sparse component analysis, and other techniques", Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); https://doi.org/10.1117/12.607004
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Cited by 2 scholarly publications.
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KEYWORDS
Independent component analysis
Error analysis
Statistical analysis
Principal component analysis
Mathematical modeling
Wavelets
Signal processing
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