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UMAP: Uniform Manifold Approximation and Projection

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McInnes, Leland, Healy, John, Saul, Nathaniel, Großberger, Lukas·2018·Journal of Open Source Software

DOI 10.21105/joss.00861

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Abstract

Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. UMAP has a rigorous mathematical foundation, but is simple to use, with a scikit-learn compatible API. UMAP is among the fastest manifold learning implementations available -significantly faster than most t-SNE implementations.

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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
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McInnes,%20Leland%20and%20Healy,%20John%20and%20Saul,%20Nathaniel%20and%20Grossberger,%20Lukas, UMAP, 2018, https://github.com/lmcinnes/umap, 2018-07-22
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“By employing the uniform manifold approximation and projection (UMAP) algorithm (McInnes et al, 2018), we transformed this 39-dimensional data into a 2-dimensional scatter plot.”
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Authors: McInnes, L., Healy, J., Saul, N., & Großberger, L.
Affiliations: Washington State University (US)
Max Planck Society (DE)
ORCID: McInnes, Leland
Healy, John
Saul, Nathaniel
Großberger, Lukas
Venue: Journal of Open Source Software
Publisher: The Open Journal
Published: 2018-09-02
Volume: 3
Issue: 29
Pages: 861
ISSN: 2475-9066 (ISSN-L)
DOI: 10.21105/joss.00861
OpenAlex: W2889326414
Cited by: 9,826
References: 2
Citations by year: 2026: 1775 · 2025: 1933 · 2024: 1675 · 2023: 1392 · 2022: 1193 · 2021: 1058 · 2020: 587 · 2019: 186 · 2018: 24
FWCI: 232.44
Type: journal article
Language: en
Open access: diamond
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
creativecommons.org/licenses/by/4.0/ (am)
creativecommons.org/licenses/by/4.0/ (tdm)
Locations: The Journal of Open Source Software (publishedVersion, cc-by)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
Keywords: UMAP, dimensionality reduction, manifold learning, nonlinear dimensionality reduction, data visualization, scikit-learn

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UMAP: Uniform Manifold Approximation and Projection

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