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---
title: 'UMAP: Uniform Manifold Approximation and Projection'
tags:
- manifold learning
- dimension reduction
- unsupervised learning
authors:
- name: Leland McInnes
orcid: 0000-0003-2143-6834
affiliation: 1
- name: John Healy
affiliation: 1
- name: Nathaniel Saul
affiliation: 2
- name: Lukas Großberger
affiliation: "3, 4"
affiliations:
- name: Tutte Institute for Mathematics and Computing
index: 1
- name: Department of Mathematics and Statistics, Washington State University
index: 2
- name: Ernst Strüngmann Institute for Neuroscience in cooperation with Max Planck Society
index: 3
- name: Donders Institute for Brain, Cognition and Behaviour, Radboud Universiteit
index: 4
date: 26 July 2018
bibliography: paper.bib
---
# Summary
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.
For details of the mathematical underpinnings see [@umap_arxiv]. The implementation
can be found at [@umap_repo].
-
# References
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