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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Bibliographic Details

Published in:	INFORMATION SCIENCES Vol. 720; p. 122520
Main Authors:	Zenil, Hector, Kiani, Narsis A., Adams, Alyssa, Abrahão, Felipe S., Rueda-Toicen, Antonio, Zea, Allan A., Ozelim, Luan, Tegnér, Jesper
Format:	Journal Article Publication
Language:	English
Published:	Elsevier Inc 01.12.2025
Subjects:	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
Online Access:	Get full text
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