wsingular is the Python package for the ICML 2022 paper "Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors".
Wasserstein Singular Vectors simultaneously compute a Wasserstein distance between samples and a Wasserstein distance between features of a dataset. These distance matrices emerge naturally as positive singular vectors of the function mapping ground costs to pairwise Wasserstein distances.
Install the package: pip install wsingular
Follow the documentation: https://wsingular.rtfd.io
The conference proceedings will be out soon. In the meantime you can cite our arXiv preprint.
@article{huizing2021unsupervised,
title={Unsupervised Ground Metric Learning using Wasserstein Eigenvectors},
author={Huizing, Geert-Jan and Cantini, Laura and Peyr{\'e}, Gabriel},
journal={arXiv preprint arXiv:2102.06278},
year={2021}
}
Code and data to reproduce the single-cell experiment are available here: https://figshare.com/s/b4904dfc0898e3837c77. For further methodological development, we advise looking into larger datasets with curated annotation, e.g. https://arxiv.org/pdf/2106.06345 (section 4.2), https://data.humancellatlas.org/ (e.g. the lung dataset), or https://cellxgene.cziscience.com/collections .