# Robust Sufficient Dimension Reduction via α-Distance Covariance

arXiv: https://arxiv.org/abs/2402.00778 · HTML: https://mlsanity.com/paper/2402.00778/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Unsupervised Feature Selection via Robust Autoencoder and Adaptive Graph Learning](https://mlsanity.com/paper/2512.18720/) (2025)

## Abstract (arXiv)

We introduce a novel sufficient dimension-reduction (SDR) method which is robust against outliers using α-distance covariance (dCov) in dimension-reduction problems. Under very mild conditions on the predictors, the central subspace is effectively estimated and model-free advantage without estimating link function based on the projection on the Stiefel manifold. We establish the convergence property of the proposed estimation under some regularity conditions. We compare the performance of our method with existing SDR methods by simulation and real data analysis and show that our algorithm improves the computational efficiency and effectiveness.