Note
The newer version of the algorithm and most up-to-date implementation is available at https://github.com/uthaipon/multi-criteria-dimensionality-reduction. The new implementations are in Python and applicable to more general classes of fair PCA problems, such as more than two groups and other fairness criteria.
Fair-PCA
Code of Fair PCA algorithm, introduced in the paper "The Price of Fair PCA: One Extra Dimension" by Samadi S, Tantipongpipat U, Morgenstern J, Singh M, and Vempala S. 32nd Conference on Neural Information Processing Systems (NIPS 2018). Please cite this paper (https://papers.nips.cc/paper/8294-the-price-of-fair-pca-one-extra-dimension.pdf) if you plan to use the code.
All codes in this project are contributed and maintained by Samira Samadi and Uthaipon (Tao) Tantipongpipat. For questions, you may contact Samira or Tao at ssamadi6@gatech.edu or tao@gatech.edu.
The data sets used in this paper belong to
-- Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst, October 2007.
-- I-Cheng Yeh and Che-hui Lien. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2): 2473–2480, 2009.
The data folder required to run this code is available here https://drive.google.com/file/d/1oj6BwlPHZap4qYPGdGKObBzEXcPiuY9Y/view?usp=sharing