# MKL-L0/1-SVM

arXiv: https://arxiv.org/abs/2308.12016 · HTML: https://mlsanity.com/paper/2308.12016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Global Convergence of ADMM in Nonconvex Nonsmooth Optimization](https://mlsanity.com/paper/1511.06324/) (2015)
- [Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application](https://mlsanity.com/paper/1309.1541/) (2013)
- [Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems](https://mlsanity.com/paper/1410.1390/) (2014)
- [Global convergence of splitting methods for nonconvex composite optimization](https://mlsanity.com/paper/1407.0753/) (2014)
- [The proximal alternating direction method of multipliers in the nonconvex setting: convergence analysis and rates](https://mlsanity.com/paper/1801.01994/) (2018)
- [Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit](https://mlsanity.com/paper/1901.02763/) (2019)
- [Quadratic Convergence of Smoothing Newton's Method for 0/1 Loss Optimization](https://mlsanity.com/paper/2103.14987/) (2021)
- [An ADMM Solver for the MKL-L0/1-SVM](https://mlsanity.com/paper/2303.04445/) (2023)

## Abstract (arXiv)

This paper presents a Multiple Kernel Learning (abbreviated as MKL) framework for the Support Vector Machine (SVM) with the (0, 1) loss function. Some KKT-like first-order optimality conditions are provided and then exploited to develop a fast ADMM algorithm to solve the nonsmooth nonconvex optimization problem. Numerical experiments on real data sets show that the performance of our MKL-L0/1-SVM is comparable with the one of the leading approaches called SimpleMKL developed by Rakotomamonjy, Bach, Canu, and Grandvalet [Journal of Machine Learning Research, vol. 9, pp. 2491-2521, 2008].