https://github.com/borealisai/eval_dr_by_wsd

Evaluating quality of dimensionality reduction map with Wasserstein distances
https://github.com/borealisai/eval_dr_by_wsd

Last synced: about 1 year ago
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Evaluating quality of dimensionality reduction map with Wasserstein distances

• Host: GitHub
• URL: https://github.com/borealisai/eval_dr_by_wsd
• Owner: BorealisAI
• License: other
• Created: 2019-01-11T00:03:38.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-01-11T00:09:24.000Z (over 7 years ago)
• Last Synced: 2025-02-16T03:35:59.083Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 190 KB
• Stars: 3
• Watchers: 5
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Evaluating quality of dimensionality reduction map with Wasserstein distances

## Code for "Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds" (NeurIPS 2018)

This repo contains the code for calculating the empirical Wasserstein many-to-one and discontinuity.

`example_swissroll_pca.ipynb` illustrates how the calculation is done.

Paper: https://papers.nips.cc/paper/8065-dimensionality-reduction-has-quantifiable-imperfections-two-geometric-bounds

Blog post: https://www.borealisai.com/en/blog/dimensionality-reduction-finally-has-quantifiable-imperfections/

bibtex entry:

```bibtex

@inproceedings{lui2018dimensionality,

  title={Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds},

  author={Lui, Kry and Ding, Gavin Weiguang and Huang, Ruitong and McCann, Robert},

  booktitle={Advances in Neural Information Processing Systems},

  pages={8462--8472},

  year={2018}

}

```

## Dependencies

* [numpy](http://www.numpy.org/)

* [scipy](https://scipy.org/scipylib/)

* [pot](https://pot.readthedocs.io/en/stable/)

* [faiss](https://github.com/facebookresearch/faiss)

* [scikit-learn](https://scikit-learn.org/stable/) (for running the examples)