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.UMAP supports a number of useful features, including the ability to use labels (or partial labels) for supervised (or semi-supervised) dimension reduction, and the ability to transform new unseen data into a pretrained embedding space.

Keywords

Projection (relational algebra)Manifold (fluid mechanics)MathematicsComputer scienceGeometryAlgorithmEngineering

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Publication Info

Year
2018
Type
article
Volume
3
Issue
29
Pages
861-861
Citations
8474
Access
Closed

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Cite This

Leland McInnes, John Healy, Nathaniel Saul et al. (2018). UMAP: Uniform Manifold Approximation and Projection. The Journal of Open Source Software , 3 (29) , 861-861. https://doi.org/10.21105/joss.00861

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DOI
10.21105/joss.00861

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Data completeness: 81%