# Scikit-dimension: a Python package for intrinsic dimension estimation arXiv: https://arxiv.org/abs/2109.02596 ยท HTML: https://mlsanity.com/paper/2109.02596/ ## Key facts - Cited by 68 papers in the map (data through 14 Oct 2026). - Builds on 17 of its 76 references that are in the map. - First posted on arXiv 6 Sep 2021. ## Builds on (most cited first, 17 of 17) - [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014) - [Estimating the intrinsic dimension of datasets by a minimal neighborhood information](https://mlsanity.com/paper/1803.06992/) (2018) - [To Trust Or Not To Trust A Classifier](https://mlsanity.com/paper/1805.11783/) (2018) - [Blessing of dimensionality: mathematical foundations of the statistical physics of data](https://mlsanity.com/paper/1801.03421/) (2018) - [Estimating the effective dimension of large biological datasets using Fisher separability analysis](https://mlsanity.com/paper/1901.06328/) (2019) - [Correction of AI systems by linear discriminants: Probabilistic foundations](https://mlsanity.com/paper/1811.05321/) (2018) - [Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension](https://mlsanity.com/paper/1904.06151/) (2019) - [On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems](https://mlsanity.com/paper/2004.04479/) (2020) - [Fractional norms and quasinorms do not help to overcome the curse of dimensionality](https://mlsanity.com/paper/2004.14230/) (2020) - [Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data](https://mlsanity.com/paper/2007.03788/) (2020) - [High-dimensional separability for one- and few-shot learning](https://mlsanity.com/paper/2106.15416/) (2021) - [The unreasonable effectiveness of small neural ensembles in high-dimensional brain](https://mlsanity.com/paper/1809.07656/) (2018) - [General stochastic separation theorems with optimal bounds](https://mlsanity.com/paper/2010.05241/) (2020) - [Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph](https://mlsanity.com/paper/1804.07580/) (2018) - [Local intrinsic dimensionality estimators based on concentration of measure](https://mlsanity.com/paper/2001.11739/) (2020) - [Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation](https://mlsanity.com/paper/2005.11107/) (2020) - [intRinsic: an R Package for Model-Based Estimation of the Intrinsic Dimension of a Dataset](https://mlsanity.com/paper/2102.11425/) (2021) ## Built on by (most cited first, 30 of 68) - [Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts](https://mlsanity.com/paper/2306.04723/) (2023) - [Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees](https://mlsanity.com/paper/2301.11911/) (2023) - [A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models](https://mlsanity.com/paper/2406.03537/) (2024) - [LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood](https://mlsanity.com/paper/2206.14882/) (2022) - [A Geometric Explanation of the Likelihood OOD Detection Paradox](https://mlsanity.com/paper/2403.18910/) (2024) - [The Intrinsic Dimension of Prompts in Internal Representations of Large Language Models](https://mlsanity.com/paper/2501.10573/) (2025) - [Topology and geometry of data manifold in deep learning](https://mlsanity.com/paper/2204.08624/) (2022) - [A Measure of the Complexity of Neural Representations based on Partial Information Decomposition](https://mlsanity.com/paper/2209.10438/) (2022) - [The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning](https://mlsanity.com/paper/2207.02797/) (2022) - [Geometry of Decision Making in Language Models](https://mlsanity.com/paper/2511.20315/) (2025) - [Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation](https://mlsanity.com/paper/2308.01054/) (2023) - [GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning](https://mlsanity.com/paper/2412.09250/) (2024) - [Impact of time and note duration tokenizations on deep learning symbolic music modeling](https://mlsanity.com/paper/2310.08497/) (2023) - [Relative intrinsic dimensionality is intrinsic to learning](https://mlsanity.com/paper/2311.07579/) (2023) - [Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning](https://mlsanity.com/paper/2409.12805/) (2024) - [Deep neural networks architectures from the perspective of manifold learning](https://mlsanity.com/paper/2306.03406/) (2023) - [Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning](https://mlsanity.com/paper/2410.13792/) (2024) - [From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures](https://mlsanity.com/paper/2511.22150/) (2025) - [Sparse Subspace Clustering for Concept Discovery (SSCCD)](https://mlsanity.com/paper/2203.06043/) (2022) - [Adversarial Estimation of Topological Dimension with Harmonic Score Maps](https://mlsanity.com/paper/2312.06869/) (2023) - [A Connection Between Score Matching and Local Intrinsic Dimension](https://mlsanity.com/paper/2510.12975/) (2025) - [Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance](https://mlsanity.com/paper/2209.03789/) (2022) - [Information-Ordered Bottlenecks for Adaptive Semantic Compression](https://mlsanity.com/paper/2305.11213/) (2023) - [Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification](https://mlsanity.com/paper/2405.15132/) (2024) - [On Convolutions, Intrinsic Dimension, and Diffusion Models](https://mlsanity.com/paper/2506.20705/) (2025) - [Automatic Layer Selection for Hallucination Detection](https://mlsanity.com/paper/2605.26366/) (2026) - [Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation](https://mlsanity.com/paper/2203.16687/) (2022) - [Multi-Sample zeta-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant](https://mlsanity.com/paper/2204.03323/) (2022) - [Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations](https://mlsanity.com/paper/2304.07686/) (2023) - [Principal subbundles for dimension reduction](https://mlsanity.com/paper/2307.03128/) (2023) ## Abstract (arXiv) 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 textttscikit-dimension, an open-source Python package for intrinsic dimension estimation. textttscikit-dimension package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface to evaluate 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 in real-life and synthetic data. The source code is available from https://github.com/j-bac/scikit-dimension , the documentation is available from https://scikit-dimension.readthedocs.io .