Fair Canonical Correlation Analysis
Fair Canonical Correlation Analysis
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DOI：
10.48550/arxiv.2309.15809
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Zhuoping Zhou;Davoud Ataee Tarzanagh;Bojian Hou;Boning Tong;Jia Xu;Yanbo Feng;Qi Long;Li Shen
中科院分区：
文献类型：
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作者：
Zhuoping Zhou;Davoud Ataee Tarzanagh;Bojian Hou;Boning Tong;Jia Xu;Yanbo Feng;Qi Long;Li Shen
本文研究了典型相关分析（CCA）中的公平性和偏差，CCA是一种广泛使用的检验两组变量之间关系的统计技术。我们提出了一个框架，通过最大限度地减少与受保护属性相关的相关差异误差来缓解不公平。我们的方法使CCA能够从所有数据点学习全局投影矩阵，同时确保这些矩阵产生与特定于组的投影矩阵相当的相关性水平。在合成数据集和真实数据集上的实验评估证明了我们的方法在减少相关视差误差而不影响CCA精度方面的有效性。
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.