2 research outputs found
A multi-objective-based approach for Fair Principal Component Analysis
In dimension reduction problems, the adopted technique may produce
disparities between the representation errors of two or more different groups.
For instance, in the projected space, a specific class can be better
represented in comparison with the other ones. Depending on the situation, this
unfair result may introduce ethical concerns. In this context, this paper
investigates how a fairness measure can be considered when performing dimension
reduction through principal component analysis. Since both reconstruction error
and fairness measure must be taken into account, we propose a
multi-objective-based approach to tackle the Fair Principal Component Analysis
problem. The experiments attest that a fairer result can be achieved with a
very small loss in the reconstruction error