mlsanityData to 14 Oct 2026

The Price of Fair PCA: One Extra Dimension

arXiv 1811.00103 ↗First posted 31 Oct 2018cs.LG stat.MLcited by 67

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Abstract (arXiv)

We investigate whether the standard dimensionality reduction technique of PCA inadvertently produces data representations with different fidelity for two different populations. We show on several real-world data sets, PCA has higher reconstruction error on population A than on B (for example, women versus men or lower- versus higher-educated individuals). This can happen even when the data set has a similar number of samples from A and B. This motivates our study of dimensionality reduction techniques which maintain similar fidelity for A and B. We define the notion of Fair PCA and give a polynomial-time algorithm for finding a low dimensional representation of the data which is nearly-optimal with respect to this measure. Finally, we show on real-world data sets that our algorithm can be used to efficiently generate a fair low dimensional representation of the data.

Key facts. Cited by 67 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Oct 2018.

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Citing papers per year: 2021: 14; 2022: 10; 2023: 9; 2024: 13; 2025: 13; 2026: 8.

67 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021On Tilted Losses in Machine Learning: Theory and Applicationscited by 43
  3. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  4. 2021Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Informationcited by 33
  5. 2021Fairness for Image Generation with Uncertain Sensitive Attributescited by 31
  6. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  7. 2023Efficient fair PCA for fair representation learningcited by 23
  8. 2021Teacher's pet: understanding and mitigating biases in distillationcited by 21
  9. 2021Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifoldcited by 18
  10. 2022Scalable Spectral Clustering with Group Fairness Constraintscited by 16
  11. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  12. 2022Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learningcited by 11
38 more
  1. 2021Wasserstein Robust Classification with Fairness Constraintscited by 10
  2. 2022Distributionally Robust Fair Principal Components via Geodesic Descentscited by 9
  3. 2021Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracingcited by 8
  4. 2022Constant-Factor Approximation Algorithms for Socially Fair k-Clusteringcited by 8
  5. 2021Pairwise Fairness for Ordinal Regressioncited by 6
  6. 2022A novel approach for Fair Principal Component Analysis based on eigendecompositioncited by 6
  7. 2024Fair CoVariance Neural Networkscited by 6
  8. 2023Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpointcited by 5
  9. 2024Fairness-Aware Estimation of Graphical Modelscited by 5
  10. 2021Lexicographically Fair Learning: Algorithms and Generalizationcited by 4
  11. 2021Feature-based Individual Fairness in k-Clusteringcited by 4
  12. 2021A Survey of Learning Criteria Going Beyond the Usual Riskcited by 4
  13. 2023Fair Column Subset Selectioncited by 4
  14. 2024Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distancescited by 4
  15. 2024Minimax Group Fairness in Strategic Classificationcited by 4
  16. 2024MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Searchcited by 4
  17. 2021Modification-Fair Cluster Editingcited by 3
  18. 2022On the Exactness of Dantzig-Wolfe Relaxation for Rank Constrained Optimization Problemscited by 3
  19. 2023Improving Fairness with Ensemble Combination: Margin-Dependent Boundscited by 3
  20. 2023On the Partial Convexification for Low-Rank Spectral Optimization: Rank Bounds and Algorithmscited by 3
  21. 2023Fair Canonical Correlation Analysiscited by 3
  22. 2024Diversity-aware clustering: Computational Complexity and Approximation Algorithmscited by 3
  23. 2024Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systemscited by 3
  24. 2023Designing Data: Proactive Data Collection and Iteration for Machine Learningcited by 2
  25. 2023Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCAcited by 2
  26. 2024Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insightscited by 2
  27. 2024Towards a Fairer Non-negative Matrix Factorizationcited by 2
  28. 2024On Socially Fair Low-Rank Approximation and Column Subset Selectioncited by 2
  29. 2022Socially Fair Center-based and Linear Subspace Clusteringcited by 1
  30. 2023When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendationscited by 1
  31. 2024Debiasing Machine Learning Models by Using Weakly Supervised Learningcited by 1
  32. 2024On the KL-Divergence-based Robust Satisficing Modelcited by 1
  33. 2025Hidden Convexity of Fair PCA and Fast Solver via Eigenvalue Optimizationcited by 1
  34. 2026Worst-case low-rank approximationscited by 1
  35. 2026Anchor PCAcited by 1
  36. 2022Cubic-Regularized Newton for Spectral Constrained Matrix Optimization and its Application to Fairnesscited by 0
  37. 2024Alpha and Prejudice: Improving α-sized Worst-case Fairness via Intrinsic Reweightingcited by 0
  38. 2025BiasGuard: Guardrailing Fairness in Machine Learning Production Systemscited by 0

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