Wasserstein Singular Vectors

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.

Get started

Install the package: pip install wsingular

Follow the documentation: https://wsingular.rtfd.io

Citing us

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}
}