{"title":"Fair Canonical Correlation Analysis.","authors":"Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Jia Xu, Yanbo Feng, Qi Long, Li Shen","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11040228/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in neural information processing systems","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.
本文研究了典型相关分析(CCA)中的公平性和偏差,CCA 是一种广泛使用的统计技术,用于研究两组变量之间的关系。我们提出了一个框架,通过最小化与受保护属性相关的相关性差异误差来缓解不公平现象。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的相关性水平与特定群体的投影矩阵相当。在合成数据集和真实数据集上进行的实验评估证明,我们的方法在降低相关性差异误差的同时,不会影响 CCA 的准确性。