# Supervised Dimensionality Reduction via Distance Correlation Maximization

arXiv: https://arxiv.org/abs/1601.00236 · HTML: https://mlsanity.com/paper/1601.00236/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2016.

## Built on by (most cited first, 7 of 7)

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- [FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation](https://mlsanity.com/paper/2406.09547/) (2024)
- [Fair Sufficient Representation Learning](https://mlsanity.com/paper/2504.01030/) (2025)

## Abstract (arXiv)

In our work, we propose a novel formulation for supervised dimensionality reduction based on a nonlinear dependency criterion called Statistical Distance Correlation, Szekely et. al. (2007). We propose an objective which is free of distributional assumptions on regression variables and regression model assumptions. Our proposed formulation is based on learning a low-dimensional feature representation z, which maximizes the squared sum of Distance Correlations between low dimensional features z and response y, and also between features z and covariates x. We propose a novel algorithm to optimize our proposed objective using the Generalized Minimization Maximizaiton method of Parizi et. al. (2015). We show superior empirical results on multiple datasets proving the effectiveness of our proposed approach over several relevant state-of-the-art supervised dimensionality reduction methods.