Fair Canonical Correlation Analysis
Zhuoping Zhou Davoud Ataee Tarzanagh Bojian Hou Boning Tong Jia Xu Yanbo Feng Qi Long Li Shen
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.