Fair Canonical Correlation Analysis

Authors

• Zhuoping Zhou
• Davoud Ataee Tarzanagh
• Bojian Hou
• Boning Tong
• Jia Xu
• Yanbo Feng
• Qi Long
• Li Shen

Language

English

Publication Date

12-1-2023

Journal

Advances in Neural Information Processing Systems

PMID

38665178

PMCID

PMC11040228

PubMedCentral® Posted Date

12-1-2024

PubMedCentral® Full Text Version

Author MSS

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.

Published Open-Access

yes

Recommended Citation

Zhou, Zhuoping; Tarzanagh, Davoud Ataee; Hou, Bojian; et al., "Fair Canonical Correlation Analysis" (2023). Faculty, Staff and Student Publications. 909.
https://digitalcommons.library.tmc.edu/uthshis_docs/909