Comparison of maximum entropy and minimal mutual information in a nonlinear setting

DOI10.1016/S0165-1684(02)00200-1zbMATH Open1002.94506OpenAlexW1995102517WikidataQ59682480 ScholiaQ59682480MaRDI QIDQ1603666

Ch. Bauer, Fabian J. Theis, Elmar Wolfgang Lang

Publication date: 15 July 2002

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

Full work available at URL: https://doi.org/10.1016/s0165-1684(02)00200-1

zbMATH Keywords

neural networksmaximum entropynonlinear independent component analysislayered neural networksminimal mutual informationnonlinear blind source separation

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 (3)

A Justification for Applying the Principle of Minimum Relative Entropy to Information Integration Problems ⋮ From \(\varepsilon\)-entropy to KL-entropy: analysis of minimum information complexity density estima\-tion ⋮ Whose entropy: a maximal entropy analysis of phosphorylation signaling

This page was built for publication: Comparison of maximum entropy and minimal mutual information in a nonlinear setting

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q1603666)