ArticleEntropy (Basel, Switzerland)2021
Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Contrastive learning to fine-tune feature extraction models for the visual cortex.PLoS computational biology · 2026Article
- Developmental tuning of functional manifold dimensionality across the human brain.bioRxiv : the preprint server for biology · 2026Article
- MDIntrinsicDimension: Dimensionality-Based Analysis of Collective Motions in Macromolecules from Molecular Dynamics Trajectories.Journal of chemical information and modeling · 2026Article
- Exploring the Architectural Biases of the Cortical Microcircuit.Neural computation · 2025Article
- The topology of molecular representations and its influence on machine learning performance.Journal of cheminformatics · 2025Article
- Lipidome visualisation, comparison, and analysis in a vector space.PLoS computational biology · 2025Article
- Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning.Scientific reports · 2025Article
- From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization.Molecular informatics · 2025Article
- Contrastive dimension reduction: when and how?Advances in neural information processing systems · 2024Article
- Article
- Exploring the Architectural Biases of the Canonical Cortical Microcircuit.bioRxiv : the preprint server for biology · 2024Article
- Multi-sampleJournal of big data · 2024Article
- Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features.Insights into imaging · 2023Article
- Distinctive features of the oropharyngeal microbiome in Inuit of Nunavik and correlations of mild to moderate bronchial obstruction with dysbiosis.Scientific reports · 2023Article
- USING MACHINE LEARNING METHODS TO ASSESS THE RISK OF ALCOHOL MISUSE IN OLDER ADULTS.Research square · 2023Article
- Intestinal microbiota links to allograft stability after lung transplantation: a prospective cohort study.Signal transduction and targeted therapy · 2023Article
- Discovery of novel JAK1 inhibitors through combining machine learning, structure-based pharmacophore modeling and bio-evaluation.Journal of translational medicine · 2023Article
- Fetal weight estimation based on deep neural network: a retrospective observational study.BMC pregnancy and childbirth · 2023Observational
- Ensuring Explainability and Dimensionality Reduction in a Multidimensional HSI World for Early XAI-Diagnostics of Plant Stress.Entropy (Basel, Switzerland) · 2023Article
- Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.