Redundancy reduction with information-preserving nonlinear maps

DOI10.1088/0954-898X/6/1/004zbMath0832.92001OpenAlexW4237311939MaRDI QIDQ4857309

Gustavo Deco, Stefan Miesbach, Lucas Parra

Publication date: 5 March 1996

Full work available at URL: https://doi.org/10.1088/0954-898x/6/1/004

zbMATH Keywords

principal component analysishigher-order statisticsdensity estimationsymplectic mapsvolume conservationunsupervised feature extractionnonlinear caseBarlow's redundancy-reduction principleentropy-preserving nonlinear maps

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

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