# Fast algorithms for large scale generalized distance weighted discrimination

arXiv: https://arxiv.org/abs/1604.05473 · HTML: https://mlsanity.com/paper/1604.05473/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2016.

## Built on by (most cited first, 2 of 2)

- [Accelerating Multi-Block Constrained Optimization Through Learning to Optimize](https://mlsanity.com/paper/2409.17320/) (2024)
- [Measure of Strength of Evidence for Visually Observed Differences between Subpopulations](https://mlsanity.com/paper/2101.00362/) (2021)

## Abstract (arXiv)

High dimension low sample size statistical analysis is important in a wide range of applications. In such situations, the highly appealing discrimination method, support vector machine, can be improved to alleviate data piling at the margin. This leads naturally to the development of distance weighted discrimination (DWD), which can be modeled as a second-order cone programming problem and solved by interior-point methods when the scale (in sample size and feature dimension) of the data is moderate. Here, we design a scalable and robust algorithm for solving large scale generalized DWD problems. Numerical experiments on real data sets from the UCI repository demonstrate that our algorithm is highly efficient in solving large scale problems, and sometimes even more efficient than the highly optimized LIBLINEAR and LIBSVM for solving the corresponding SVM problems.