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Fair Canonical Correlation Analysis.

Zhuoping Zhou1, Davoud Ataee Tarzanagh1, Bojian Hou1

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Summary
This summary is machine-generated.

This study introduces a new method to reduce bias in Canonical Correlation Analysis (CCA), ensuring fairer relationships between variables. The approach minimizes correlation disparity without sacrificing analytical accuracy.

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Canonical Correlation Analysis (CCA) is a statistical method to analyze relationships between variable sets.
  • Existing CCA methods can exhibit bias related to protected attributes.
  • Fairness in statistical analysis is crucial for unbiased insights.

Purpose of the Study:

  • To develop a framework for mitigating bias in Canonical Correlation Analysis (CCA).
  • To minimize correlation disparity error linked to protected attributes.
  • To ensure fairness in statistical variable relationship analysis.

Main Methods:

  • A novel framework is proposed to alleviate unfairness in CCA.
  • The method minimizes correlation disparity error by learning global projection matrices.
  • Ensures comparable correlation levels to group-specific matrices.

Main Results:

  • Experimental results on synthetic and real-world datasets confirm the method's effectiveness.
  • Demonstrated reduction in correlation disparity error.
  • Maintained or improved CCA accuracy.

Conclusions:

  • The proposed framework successfully reduces bias in CCA.
  • Achieves fairness by minimizing correlation disparity without compromising accuracy.
  • Offers a more equitable approach to analyzing relationships between variable sets.