Convex Formulations for Fair Principal Component Analysis

Understand

Though there is a growing body of literature on fairness for supervised learning, the problem of incorporating fairness into unsupervised learning has been less well-studied.

• This paper studies fairness in the context of principal component analysis (PCA).
• We first present a definition of fairness for dimensionality reduction, and our definition can be interpreted as saying that a reduction is fair if information about a protected class (e.g., race or gender) cannot be inferred from the dimensionality-reduced data points.
• Next, we develop convex optimization formulations that can improve the fairness (with respect to our definition) of PCA and kernel PCA.