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UMAP: Uniform Manifold Approximation and Projection
Open accessAbstract
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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“Other methods, such as t-distributed stochastic neighbor embedding (t-SNE) (68) or uniform manifold approximation and projection (UMAP) (69), aim to preserve apparent pairwise distances approximately in a reduced two-dimensional space so that it can be readily visualized.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [69]
In the citing paper
“Uniform Manifold Approximation and Projection (UMAP) [58] is a successor of t-SNE, superior in run time performance, scalability regarding larger datasets, and perseverance of data structure.”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [58]
In the citing paper
“Lack of maturity”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [58]
In the citing paper
“Improved time performance, Better perseverance of data structure”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [58]
Citations
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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
Self-citationMcInnes,%20Leland%20and%20Healy,%20John%20and%20Saul,%20Nathaniel%20and%20Grossberger,%20Lukas, UMAP, 2018, https://github.com/lmcinnes/umap, 2018-07-22
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Method
Metadata
- Authors
- McInnes, L., Healy, J., Saul, N., & Großberger, L.
- Affiliations
- Washington State University (US)
- Max Planck Society (DE)
- 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)
- 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
- 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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