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Basics of Multivariate Analysis in Neuroimaging Data

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Fair CCA for Fair Representation Learning: An ADNI Study.

Bojian Hou¹, Zhanliang Wang¹, Zhuoping Zhou¹

¹University of Pennsylvania, Philadelphia, Pennsylvania, USA.

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|January 5, 2026

Summary

This summary is machine-generated.

We developed a new fair canonical correlation analysis (CCA) method to improve fairness in machine learning. This approach enhances unbiased representation learning for classification tasks without sacrificing accuracy.

Keywords:

Alzheimer’s Disease CCA Fairness Representation Learning

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Area of Science:

Machine Learning
Data Science
Neuroimaging

Background:

Canonical Correlation Analysis (CCA) identifies relationships between data modalities.
Fairness in machine learning is increasingly important.
Existing fair CCA methods may negatively impact downstream tasks.

Purpose of the Study:

To propose a novel fair CCA method for fair representation learning.
To ensure projected features are independent of sensitive attributes.
To enhance fairness in classification tasks without compromising accuracy.

Main Methods:

Developed a novel fair CCA algorithm.
Ensured independence of projected features from sensitive attributes.
Validated on synthetic and Alzheimer's Disease Neuroimaging Initiative (ADNI) data.

Main Results:

The proposed method maintains high CCA performance.
Demonstrated improved fairness in downstream classification tasks.
Successfully applied to real-world neuroimaging data.

Conclusions:

The novel fair CCA method enhances fairness and maintains performance.
Enables unbiased machine learning in sensitive domains like neuroimaging.
Offers a practical solution for fair representation learning in classification.