Information-theoretic approach to blind separation of sources in non-linear mixture

DOI10.1016/S0165-1684(97)00196-5zbMath1006.94514OpenAlexW2033791041MaRDI QIDQ1129227

Andrzej Cichocki, Howard Hua Yang, Shun-ichi Amari

Publication date: 13 August 1998

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0165-1684(97)00196-5

zbMATH Keywords

maximum entropyminimum mutual informationblind separationnonlinear mixtureinformation back-propagation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Measures of information, entropy (94A17)

Related Items (8)

gpICA: a novel nonlinear ICA algorithm using geometric linearization ⋮ Kernel-Based Nonlinear Blind Source Separation ⋮ Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures ⋮ A high order cumulants based multivariate nonlinear blind source separation method ⋮ A high order cumulants based multivariate nonlinear blind source separation method ⋮ Entropy embedding and fluctuation analysis in genomic manifolds ⋮ Using Signal Invariants to Perform Nonlinear Blind Source Separation ⋮ Independent Slow Feature Analysis and Nonlinear Blind Source Separation

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