Fair CCA for Fair Representation Learning: An ADNI Study

TL;DR

This paper introduces a novel fair CCA method that ensures independence from sensitive attributes, improving fairness in neuroimaging classification tasks without sacrificing correlation analysis performance.

Contribution

The paper presents a new fair CCA approach that enhances fairness in representation learning, specifically tailored for neuroimaging data, while maintaining high correlation analysis accuracy.

Findings

Effective in maintaining high correlation analysis performance.

Improves fairness in classification tasks on ADNI data.

Validated on synthetic and real-world neuroimaging data.

Abstract

Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes crucial in machine learning, fair CCA has gained attention. However, previous approaches often overlook the impact on downstream classification tasks, limiting applicability. We propose a novel fair CCA method for fair representation learning, ensuring the projected features are independent of sensitive attributes, thus enhancing fairness without compromising accuracy. We validate our method on synthetic data and real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), demonstrating its ability to maintain high correlation analysis performance while improving fairness in classification tasks. Our work enables fair machine learning in neuroimaging studies where unbiased analysis is essential. Code is available in https://github.com/ZhanliangAaronWang/FR-CCA-ADNI.