Fair Canonical Correlation Analysis

Published: 21 Sept 2023, Last Modified: 16 Jan 2024NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: Fairness, Canonical Correlation Analysis, Riemannian Optimization, Pareto Optimization
TL;DR: The paper introduces a framework to address fairness and bias in Canonical Correlation Analysis, ensuring comparable correlation levels across groups without sacrificing accuracy.
Abstract: This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables. We present a framework that alleviates unfairness by minimizing the correlation disparity error associated with protected attributes. Our approach enables CCA to learn global projection matrices from all data points while ensuring that these matrices yield comparable correlation levels to group-specific projection matrices. Experimental evaluation on both synthetic and real-world datasets demonstrates the efficacy of our method in reducing correlation disparity error without compromising CCA accuracy.
Supplementary Material: zip
Submission Number: 12083