Fair principal component analysis via eigenvalue optimization

Key Points

• Effective integration of eigenvalue optimization enhances fair principal component analysis, addressing data representation issues.
• Key findings show that this method significantly reduces bias, improving the representation of underrepresented groups in the dataset.
• Implementation involves utilizing eigenvalue optimization techniques to enhance fairness in principal component analysis.
• These results highlight the need for fairness considerations in data representation strategies, suggesting further exploration in various domains.