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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Veröffentlicht in:	INFORMATION SCIENCES Jg. 720; S. 122520
Hauptverfasser:	Zenil, Hector, Kiani, Narsis A., Adams, Alyssa, Abrahão, Felipe S., Rueda-Toicen, Antonio, Zea, Allan A., Ozelim, Luan, Tegnér, Jesper
Format:	Journal Article Verlag
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.12.2025
Schlagworte:	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-Zugang:	Volltext
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