mlsanityData to 7 Oct 2026

Fair Canonical Correlation Analysis

arXiv 2309.15809 ↗First posted 27 Sep 2023cs.LG stat.MLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 83 references that are in the map. First posted on arXiv 27 Sep 2023.

Builds on

21 of 83 references.

References · 21

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2020Fairness in Machine Learning: A Surveycited by 369
  3. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  4. 2020Fairness in Machine Learningcited by 194
  5. 2018Fair Clustering Through Fairletscited by 176
  6. 2018The Frontiers of Fairness in Machine Learningcited by 139
  7. 2017Ranking with Fairness Constraintscited by 78
  8. 2019Guarantees for Spectral Clustering with Fairness Constraintscited by 72
  9. 2018The Price of Fair PCA: One Extra Dimensioncited by 67
  10. 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 66
  11. 2016Global rates of convergence for nonconvex optimization on manifoldscited by 61
  12. 2022FedNest: Federated Bilevel, Minimax, and Compositional Optimizationcited by 60
9 more
  1. 2022AutoBalance: Optimized Loss Functions for Imbalanced Datacited by 49
  2. 2020Projection Robust Wasserstein Distance and Riemannian Optimizationcited by 45
  3. 2023Efficient fair PCA for fair representation learningcited by 23
  4. 2019Efficient Fair Principal Component Analysiscited by 20
  5. 2017A Tutorial on Canonical Correlation Methodscited by 14
  6. 2022Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methodscited by 14
  7. 2021An Online Riemannian PCA for Stochastic Canonical Correlation Analysiscited by 7
  8. 2016Application of multiview techniques to NHANES datasetcited by 1
  9. 2019Iteration-complexity and asymptotic analysis of steepest descent method for multiobjective optimization on Riemannian manifoldscited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Fairness-Aware Estimation of Graphical Modelscited by 5
  2. 2025Graph Canonical Correlation Analysiscited by 1
  3. 2025Fair CCA for Fair Representation Learning: An ADNI Studycited by 1