wsingular 0.1.7

Wasserstein Singular Vectors

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}
}