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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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“Dimension reduction was performed using principal component analysis (PCA) and then uniform manifold approximation and projection (UMAP) on the top 11–20 principal components (PCs) and 15–30 nearest neighbors for visualization on 2 dimensions (McInnes et al., 2018; Polański et al., 2019).”
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“For example, t -distributed stochastic neighbor embedding 19 and uniform manifold approximation and projection (UMAP) 20 are two widely used algorithms for this purpose, despite recent concerns regarding their reliability and validity 21 .”
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“The clusters (for one particular experiment out of the 10 of them) are shown in Figure 2 after applying UMAP (McInnes et al., 2018; McInnes et al., 2018) to the contextualized embeddings.”
Method
A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition
Kim, K. K., Kim, M., Pyun, K. R., Kim, J., Min, J., Koh, S., Root, S. E., Kim, J., Nguyen, B. T., Nishio, Y., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00888-7 · Cited by 185
“The joint signals of PIP, MCP and wrist are clearly represented in the uniform manifold approximation and projection (UMAP) 42–46 (Extended Data Fig.”
Background

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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