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Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

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journal contribution
posted on 2022-05-17, 09:25 authored by Jonathan Bac, Evgeny M Mirkes, Alexander N Gorban, Ivan Tyukin, Andrei Zinovyev
Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data.

Funding

The work was supported by the Ministry of Science and Higher Education of the Russian Federation (Project No. 075-15-2020-927), by the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’Avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), by the Association Science et Technologie, the Institut de Recherches Internationales Servier and the doctoral school Frontières de l’Innovation en Recherche et Education Programme Bettencourt. I.T. was supported by the UKRI Turing AI Acceleration Fellowship (EP/V025295/1).

History

Citation

Bac, J.; Mirkes, E.M.; Gorban, A.N.; Tyukin, I.; Zinovyev, A. Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation. Entropy 2021, 23, 1368. https://doi.org/10.3390/e23101368

Author affiliation

Department of Mathematics

Version

  • VoR (Version of Record)

Published in

Entropy

Volume

23

Issue

10

Pagination

1368

Publisher

MDPI

issn

1099-4300

eissn

1099-4300

Acceptance date

2021-10-16

Copyright date

2021

Available date

2021-10-19

Language

en

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