Dimensionality reduction is a classical technique widely used for data analysis. One foundational instantiation is Principal Component Analysis (PCA), which minimizes the average reconstruction error. In this paper, we introduce the multi-criteria dimensionality reduction problem where we are given multiple objectives that need to be optimized simultaneously. As an application, our model captures several fairness criteria for dimensionality reduction such as the Fair-PCA problem introduced by Samadi et al. [NeurIPS18] and the Nash Social Welfare (NSW) problem. In the Fair-PCA problem, the input data is divided into k groups, and the goal is to find a single d-dimensional representation for all groups for which the maximum reconstruction error of any one group is minimized.

Originality: The authors' solve a problem left open in a previous paper and made strictly improve on previous work for approximation algorithms. They do so by giving new insights into structural properties of extreme points of semi-definite programs and more general convex programs. As far as I understand, the algorithms presented in the paper are not substantially new, but the analysis of these algorithms is novel. Quality: The paper seems complete and the proofs appear correct. The paper tackles interesting problems and reaches satisfying conclusions. They essentially close the book on the k 2 case, make significant improvements for k 2 and leave open some questions for structured data.

Recent work of Samadi et al. introduced the fair PCA problem where the goal is to find a projection that minimizes the error and furthermore the error is balanced across two groups in the population. The takeaway message from their paper was that adding one extra dimension is enough. Firstly, this paper resolves the main open question from the work of Samadi et al. by giving an algorithm that does not need to increase the dimension by one. Second, they push the framework fo fair PCA in some interesting new directions that allow for alternative notions of fairness and multiple groups. They improve the fairness penalty to be \sqrt{k} from k-1.

Dimensionality reduction is a classical technique widely used for data analysis. One foundational instantiation is Principal Component Analysis (PCA), which minimizes the average reconstruction error. In this paper, we introduce the multi-criteria dimensionality reduction problem where we are given multiple objectives that need to be optimized simultaneously. As an application, our model captures several fairness criteria for dimensionality reduction such as the Fair-PCA problem introduced by Samadi et al. [NeurIPS18] and the Nash Social Welfare (NSW) problem. In the Fair-PCA problem, the input data is divided into k groups, and the goal is to find a single d-dimensional representation for all groups for which the maximum reconstruction error of any one group is minimized.

Dimensionality reduction is a classical technique widely used for data analysis. One foundational instantiation is Principal Component Analysis (PCA), which minimizes the average reconstruction error. In this paper, we introduce the multi-criteria dimensionality reduction problem where we are given multiple objectives that need to be optimized simultaneously. As an application, our model captures several fairness criteria for dimensionality reduction such as the Fair-PCA problem introduced by Samadi et al. [NeurIPS18] and the Nash Social Welfare (NSW) problem. In the Fair-PCA problem, the input data is divided into k groups, and the goal is to find a single d-dimensional representation for all groups for which the maximum reconstruction error of any one group is minimized.