# Sparse Distance Weighted Discrimination

arXiv: https://arxiv.org/abs/1501.06066 · HTML: https://mlsanity.com/paper/1501.06066/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2015.

## Built on by (most cited first, 3 of 3)

- [Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2105.00292/) (2021)
- [Multiway sparse distance weighted discrimination](https://mlsanity.com/paper/2110.05377/) (2021)
- [TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection](https://mlsanity.com/paper/2606.06742/) (2026)

## Abstract (arXiv)

Distance weighted discrimination (DWD) was originally proposed to handle the data piling issue in the support vector machine. In this paper, we consider the sparse penalized DWD for high-dimensional classification. The state-of-the-art algorithm for solving the standard DWD is based on second-order cone programming, however such an algorithm does not work well for the sparse penalized DWD with high-dimensional data. In order to overcome the challenging computation difficulty, we develop a very efficient algorithm to compute the solution path of the sparse DWD at a given fine grid of regularization parameters. We implement the algorithm in a publicly available R package sdwd. We conduct extensive numerical experiments to demonstrate the computational efficiency and classification performance of our method.