A novel approach for Fair Principal Component Analysis based on eigendecomposition
This work addresses fairness concerns in PCA for applications like signal processing, offering a simple solution to reduce bias towards sensitive groups, though it is incremental compared to existing fair PCA methods.
The authors tackled the problem of fairness in principal component analysis (PCA) by proposing a novel algorithm that uses a one-dimensional search based on eigendecomposition, which significantly improved fairness with minimal loss in reconstruction error, as shown in numerical experiments.
Principal component analysis (PCA), a ubiquitous dimensionality reduction technique in signal processing, searches for a projection matrix that minimizes the mean squared error between the reduced dataset and the original one. Since classical PCA is not tailored to address concerns related to fairness, its application to actual problems may lead to disparity in the reconstruction errors of different groups (e.g., men and women, whites and blacks, etc.), with potentially harmful consequences such as the introduction of bias towards sensitive groups. Although several fair versions of PCA have been proposed recently, there still remains a fundamental gap in the search for algorithms that are simple enough to be deployed in real systems. To address this, we propose a novel PCA algorithm which tackles fairness issues by means of a simple strategy comprising a one-dimensional search which exploits the closed-form solution of PCA. As attested by numerical experiments, the proposal can significantly improve fairness with a very small loss in the overall reconstruction error and without resorting to complex optimization schemes. Moreover, our findings are consistent in several real situations as well as in scenarios with both unbalanced and balanced datasets.