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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]
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“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]
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“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]
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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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MethodInfluentialShi, C., Cao, M. C., Rehn, S. M., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00793-9 · Cited by 58
“The other feature extraction method we tested was UMAP, which approximates the features in low-dimensional manifold space.”
BackgroundInfluentialSuri, J. S., Agarwal, S., Chabert, G. L., Carriero, A., Paschè, A., Danna, P. S. C., Saba, L., Mehmedović, A., Faa, G., Singh, I. M., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12061482 · Cited by 58
Assessing the uncertainty of deep learning soil spectral models using Monte Carlo dropout
Padarian, J., Minasny, B., & McBratney, A. B. (2022) · Geoderma · doi:10.1016/j.geoderma.2022.116063 · Cited by 57
Lipidomic signatures align with inflammatory patterns and outcomes in critical illness
Wu, J., Cyr, A. R., Gruen, D. S., Lovelace, T. C., Benos, P. V., Das, J., Kar, U. K., Chen, T., Guyette, F. X., Yazer, M. H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34420-4 · Cited by 63
Vela, B., Acemi, C., Singh, P. K., et al. (2023) · Acta Materialia · doi:10.1016/j.actamat.2023.118784 · Cited by 58
Wang, D., Hu, X., Ye, H., Wang, Y., Yang, Q., Liang, X., Wang, Z., Zhou, Y., Wen, M., Yuan, X., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02886-0 · Cited by 60
“We performed dimensionality reduction analysis for the WT and fl scRNA-seq data with Uniform Manifold Approximation and Projection (UMAP) [35].”
MethodAnuragi, A., Sisodia, D. S., & Pachori, R. B. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10711-8 · Cited by 74
Property-guided generation of complex polymer topologies using variational autoencoders
Jiang, S., Dieng, A. B., & Webb, M. A. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01328-0 · Cited by 70
Precocious neuronal differentiation and disrupted oxygen responses in Kabuki syndrome
Carosso, G. A., Boukas, L., Augustin, J. J., Nguyen, H. N., Winer, B. L., Cannon, G. H., Robertson, J. D., Zhang, L., Hansen, K. D., Goff, L. A., et al. (2019) · JCI Insight · doi:10.1172/jci.insight.129375 · Cited by 57
“Dimensionality reduction and visualization were performed via UMAP (31) on the log10(counts + 1) of the high-variance genes in the NSPC data set.”
MethodInfluentialProbing infectious disease by single-cell RNA sequencing: Progresses and perspectives
Luo, G., Gao, Q., Zhang, S., & Yan, B. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.10.016 · Cited by 61
Vallejo‐Marín, M., Friedman, J., Twyford, A. D., Lepais, O., Ickert‐Bond, S. M., Streisfeld, M. A., Yant, L. J., van Kleunen, M., Rotter, M. C., & Puzey, J. R. (2021) · Communications Biology · doi:10.1038/s42003-021-01795-x · Cited by 58
Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-MakingPreprint
Santos, A., Colaço, A. R., Nielsen, A. B., Niu, L., Geyer, P. E., Coscia, F., Albrechtsen, N. J. W., Mundt, F. O., Jensen, L. J., & Mann, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.09.084897 · Cited by 59
“Next, the default data analysis used PCA to perform feature reduction on the data ( processed data frame) (alternatively, t-SNE or UMAP) (Figure 4D).”
BackgroundMethodSykas, D., Sdraka, M., Zografakis, D., & Papoutsis, I. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3164771 · Cited by 59
“In order to fortify this observation, different UMAP [62] visualizations are provided in Fig.”
MethodPenny‐Dimri, J. C., Bergmeir, C., Reid, C. M., Williams‐Spence, J., Cochrane, A. D., & Smith, J. A. (2020) · Seminars in Thoracic and Cardiovascular Surgery · doi:10.1053/j.semtcvs.2020.09.028 · Cited by 56
A Short Review on Minimum Description Length: An Application to Dimension Reduction in PCA
Bruni, V., Cardinali, M. L., & Vitulano, D. (2022) · Entropy · doi:10.3390/e24020269 · Cited by 60
Improving de novo molecular design with curriculum learning
Guo, J., Fialková, V., Arango, J. D., Margreitter, C., Janet, J. P., Papadopoulos, K., Engkvist, O., & Patronov, A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00494-4 · Cited by 61
A Novel Bayesian-Optimization-Based Adversarial TCN for RUL Prediction of Bearings
Chen, Q., Liu, Y.-B., Ge, M., Liu, J., & Wang, L. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3209894 · Cited by 57
“Recently, McInnes et al. [28] proposed the uniform manifold approximation and projection (UMAP) for dimensionality reduction, which can solve the above problems well [29].”
BackgroundStriated muscle-specific base editing enables correction of mutations causing dilated cardiomyopathy
Grosch, M., Schraft, L., Chan, A., Küchenhoff, L., Rapti, K., Ferreira, A.-M., Kornienko, J., Li, S., Radke, M. H., Krämer, C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39352-1 · Cited by 61
Diao, Y., Liu, D., Ge, H., Zhang, R., Jiang, K., Bao, R., Zhu, X., Bi, H., Liao, W., Chen, Z., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40219-8 · Cited by 60
Cross-modal representation of identity in the primate hippocampus
Tyree, T. J., Metke, M., & Miller, C. T. (2023) · Science · doi:10.1126/science.adf0460 · Cited by 55
“The UMAP algorithm was composed of two steps that can fruitfully be described as graph construction and graph projection ( 38 ).”
MethodMolPROP: Molecular Property prediction with multimodal language and graph fusion
Rollins, Z. A., Cheng, A. C., & Metwally, E. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00846-9 · Cited by 71
“Finally, we explore the learned latent embeddings representations of the MolPROP GATv2-ChemBERTa-2-77 M-MLM model by projecting the latent embeddings onto a 2D space using UMAP [44] (Fig.”
MethodMulti-omics data integration and analysis pipeline for precision medicine: Systematic review
Abdelaziz, E. H., Ismail, R. M., Mabrouk, M. S., & Amin, E. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108254 · Cited by 67
Deep learning reveals antibiotics in the archaeal proteome
Torres, M. D. T., Wan, F., & de la Fuente‐Núñez, C. (2025) · Nature Microbiology · doi:10.1038/s41564-025-02061-0 · Cited by 57
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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