Fair Canonical Correlation Analysis

• URL: http://arxiv.org/abs/2309.15809v1
• Date: Wed, 27 Sep 2023 17:34:13 GMT
• Title: Fair Canonical Correlation Analysis
• Authors: Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Jia Xu, Yanbo Feng, Qi Long, Li Shen
• Abstract summary: Canonical Correlation Analysis (CCA) is a widely used 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.
• Score: 14.206538828733507
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• The radius of statistical efficiency [8.771678221101368]
  We introduce a measure of robustness of an estimation problem: the radius of statistical efficiency (RSE) RSE is the size of the smallest perturbation to the problem data that renders the Fisher information matrix singular. We observe a precise reciprocal relationship between RSE and the intrinsic complexity/sensitivity of the problem instance, paralleling the classical Eckart-Young theorem in numerical analysis.
  arXiv  Detail & Related papers  (2024-05-15T19:36:08Z)
• Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification [72.77513633290056]
  We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessian matrix evaluated on a deep learning model. Our method captures intricate patterns and relationships, enhancing classification performance.
  arXiv  Detail & Related papers  (2024-02-14T16:10:42Z)
• Correlation visualization under missing values: a comparison between imputation and direct parameter estimation methods [4.963490281438653]
  We compare the effects of various missing data methods on the correlation plot, focusing on two common missing patterns: random and monotone. We recommend using DPER, a direct parameter estimation approach, for plotting the correlation matrix based on its performance in the experiments.
  arXiv  Detail & Related papers  (2023-05-10T10:52:30Z)
• Direct-Effect Risk Minimization for Domain Generalization [11.105832297850188]
  We introduce the concepts of direct and indirect effects from causal inference to the domain generalization problem. We argue that models that learn direct effects minimize the worst-case risk across correlation-shifted domains. Experiments on 5 correlation-shifted datasets and the DomainBed benchmark verify the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-11-26T15:35:36Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• A connection between the pattern classification problem and the General Linear Model for statistical inference [0.2320417845168326]
  Both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains. We derive a statistical test based on a more refined predictive algorithm. The MLE-based inference employs a residual score and includes the upper bound to compute a better estimation of the actual (real) error.
  arXiv  Detail & Related papers  (2020-12-16T12:26:26Z)
• Out-of-distribution Generalization via Partial Feature Decorrelation [72.96261704851683]
  We present a novel Partial Feature Decorrelation Learning (PFDL) algorithm, which jointly optimize a feature decomposition network and the target image classification model. The experiments on real-world datasets demonstrate that our method can improve the backbone model's accuracy on OOD image classification datasets.
  arXiv  Detail & Related papers  (2020-07-30T05:48:48Z)
• Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
  This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets. Part of the challenge of learning robust models lies in the influence of unobserved confounders. We demonstrate the empirical performance of our approach on healthcare data from different modalities.
  arXiv  Detail & Related papers  (2020-07-21T08:18:06Z)
• On Disentangled Representations Learned From Correlated Data [59.41587388303554]
  We bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data. We show that systematically induced correlations in the dataset are being learned and reflected in the latent representations. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.
  arXiv  Detail & Related papers  (2020-06-14T12:47:34Z)
• Asymptotic Analysis of an Ensemble of Randomly Projected Linear Discriminants [94.46276668068327]
  In [1], an ensemble of randomly projected linear discriminants is used to classify datasets. We develop a consistent estimator of the misclassification probability as an alternative to the computationally-costly cross-validation estimator. We also demonstrate the use of our estimator for tuning the projection dimension on both real and synthetic data.
  arXiv  Detail & Related papers  (2020-04-17T12:47:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.