Minimal algorithmic information loss methods for dimension reduction, feature selection and network sparsification

We present a novel, domain-agnostic, model-independent, unsupervised, and universally applicable Machine Learning approach for dimensionality reduction based on the principles of algorithmic complexity. Specifically, but without loss of generality, we focus on addressing the challenge of reducing ce...

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Vydané v:	INFORMATION SCIENCES Ročník 720; s. 122520
Hlavní autori:	Zenil, Hector, Kiani, Narsis A., Adams, Alyssa, Abrahão, Felipe S., Rueda-Toicen, Antonio, Zea, Allan A., Ozelim, Luan, Tegnér, Jesper
Médium:	Journal Article Publikácia
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.12.2025
Predmet:	Algorithmic image segmentation Data dimensionality reduction Lossy algorithmic complexity Machine learning Network complexity Recursive compression Algorithmic image segmentation Data dimensionality reduction Lossy algorithmic complexity Recursive compression Machine learning Network complexity
ISSN:	0020-0255
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