Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

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 be...

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Vydáno v:Entropy (Basel, Switzerland) Ročník 23; číslo 10; s. 1368
Hlavní autoři: Bac, Jonathan, Mirkes, Evgeny M., Gorban, Alexander N., Tyukin, Ivan, Zinovyev, Andrei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 19.10.2021
MDPI
Témata:
Algorithms
Application programming interface
Artificial Intelligence
Computer Science
Datasets
Documentation
effective dimension
Fractals
intrinsic dimension
Life Sciences
Machine Learning
method benchmarking
Methods
Open source software
Principal components analysis
Public domain
Python package
Quality assessment
Quantitative Methods
Technical Note
method benchmarking
effective dimension
Python package
intrinsic dimension
ISSN:1099-4300, 1099-4300
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Shrnutí: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.
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PMCID: PMC8534554
ISSN:1099-4300
1099-4300
DOI:10.3390/e23101368