[NEW SUBMISSION] umap-learn

[cakiki]

Citation information

"umap-learn": {
    "tags": [
        "mcinnes2018umap-software",
        "2018arXivUMAP"
    ],
    "logo": "img/umap-learn.png",
    "language": "Python",
    "category": "Visualisation",
    "keywords": [
        "visualization",
        "machine-learning",
        "dimensionality-reduction",
        "umap",
        "topological-data-analysis"
    ],
    "description": "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.",
    "link": "https://umap-learn.readthedocs.io/",
    "attribution_link": "https://github.com/lmcinnes/umap?tab=readme-ov-file#citation",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "numpy",
        "scipy",
        "Numba",
        "scikit-learn",
        "tqdm"
    ]
}

BibTeX

@article{mcinnes2018umap-software,
  title={UMAP: Uniform Manifold Approximation and Projection},
  author={McInnes, Leland and Healy, John and Saul, Nathaniel and Grossberger, Lukas},
  journal={The Journal of Open Source Software},
  volume={3},
  number={29},
  pages={861},
  year={2018}
}

@article{2018arXivUMAP,
     author = {{McInnes}, L. and {Healy}, J. and {Melville}, 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,
}

Logo can be found here: https://github.com/lmcinnes/umap/blob/master/doc/logo_large.png (Not sure whether it'd be better not to include the text, or whether Leland can point us to a version of the logo better suited for this use case.)

[cakiki]

There are also additional bibtex entries in the UMAP repo that correspond to particular settings of the algorithm that I did not include here.

[FloorBroekgaarden]

Thanks so much @cakiki - this looks great.

I added it with commit b25e48e. It should appear on the website within ~5 min.

• I actually like that the Logo has text, but happy to replace it with a different logo anytime in the future if preferred - juts let us know!
• thank you for pointing to the potential additional references that users might want to cite. We have a way to include this and encourage this. I created a custom citation that says: "custom_citation": "This research made use of \\texttt{umap-learn} \\citep{mcinnes2018umap-software, 2018arXivUMAP}\\footnote{{TODO}: include the following if used: the densMAP algorithm \\citep{NBC2020} and the Parametric UMAP algorithm \\citep{SMG2020}.}", -> the result is that when people click on umap-learn that it will not only include the citation, but also add a footnote in the text that tells users to check whether they need to cite the additional packages. We hope this encourages people to cite the additional packages when used.

Feel free to check it and let us know if you have any suggestions ! Happy to help!

Thanks !