Files for JOSS submission.

paper.bib

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+@article{umap_arxiv,
+     author = {{McInnes}, L. and {Healy}, J.},
+     title = "{UMAP: Uniform Manifold Approximation
+     and Projection for Dimension Reduction}",
+     journal = {ArXiv e-prints},
+     archivePrefix = "arXiv",
+     eprint = {1802.03426},
+     primaryClass = "stat.ML",
+     keywords = {Statistics - Machine Learning,
+                 Computer Science - Computational Geometry,
+                 Computer Science - Learning},
+     year = 2018,
+     month = feb,
+}

paper.md

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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 -- signifcantly 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].
+
+-![Fashion MNIST embedded via UMAP](images/umap_example_fashion_mnist1.png)
+
+# References