Uncertainty-aware principal component analysis

This is an implementation of uncertainty-aware principal component analysis, which generalizes PCA to work on probability distributions.

You can find a preprint of our paper at arXiv:1905.01127 or on my personal website. We also extracted means and covariances from the student grades dataset.

Development

The dependencies can be install using yarn:

yarn install

Builds can be prepared using:

yarn run build
yarn run dev # watches for changes

Run tests:

yarn run test

To perform linter checks you there is:

yarn run lint
yarn run lint-fix # tries to fix some of the warnings

Citation

To cite this work, you can use the following BibTex entry:

@article{UaPCA:2020,
  author    = {Jochen Görtler and Thilo Spinner and Dirk Streeb and Daniel Weiskopf and Oliver Deussen},
  title     = {Uncertainty-Aware Principal Component Analysis},
  journal   = {IEEE Transactions on Visualization and Computer Graphics},
  year      = {2020},
  pages     = {to appear}
}