Efficient fair PCA for fair representation learning
Machine Learning
2023-02-28 v1 Computers and Society
Machine Learning
Abstract
We revisit the problem of fair principal component analysis (PCA), where the goal is to learn the best low-rank linear approximation of the data that obfuscates demographic information. We propose a conceptually simple approach that allows for an analytic solution similar to standard PCA and can be kernelized. Our methods have the same complexity as standard PCA, or kernel PCA, and run much faster than existing methods for fair PCA based on semidefinite programming or manifold optimization, while achieving similar results.
Keywords
Cite
@article{arxiv.2302.13319,
title = {Efficient fair PCA for fair representation learning},
author = {Matthäus Kleindessner and Michele Donini and Chris Russell and Muhammad Bilal Zafar},
journal= {arXiv preprint arXiv:2302.13319},
year = {2023}
}