Achieving Fair PCA Using Joint Eigenvalue Decomposition

• URL: http://arxiv.org/abs/2502.16933v1
• Date: Mon, 24 Feb 2025 07:51:02 GMT
• Title: Achieving Fair PCA Using Joint Eigenvalue Decomposition
• Authors: Vidhi Rathore, Naresh Manwani,
• Abstract summary: Principal Component Analysis is a widely used method for dimensionality reduction.<n>It often overlooks fairness, especially when working with data that includes demographic characteristics.<n>We show that our approach incorporates Joint Eigenvalue Decomposition (JEVD), a technique that enables the simultaneous diagonalization of multiple matrices.
• Score: 3.667856249968452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Principal Component Analysis (PCA) is a widely used method for dimensionality reduction, but it often overlooks fairness, especially when working with data that includes demographic characteristics. This can lead to biased representations that disproportionately affect certain groups. To address this issue, our approach incorporates Joint Eigenvalue Decomposition (JEVD), a technique that enables the simultaneous diagonalization of multiple matrices, ensuring fair and efficient representations. We formally show that the optimal solution of JEVD leads to a fair PCA solution. By integrating JEVD with PCA, we strike an optimal balance between preserving data structure and promoting fairness across diverse groups. We demonstrate that our method outperforms existing baseline approaches in fairness and representational quality on various datasets. It retains the core advantages of PCA while ensuring that sensitive demographic attributes do not create disparities in the reduced representation.

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