MKL-$L_{0/1}$-SVM

TL;DR

This paper introduces a novel MKL framework for SVM with an $(0, 1)$ loss, providing optimality conditions and a fast ADMM algorithm, demonstrating competitive performance on real datasets.

Contribution

It develops a new MKL-$L_{0/1}$-SVM with a specialized optimization algorithm and analyzes its optimality conditions.

Findings

Performance comparable to leading approaches like SimpleMKL

Develops a fast ADMM algorithm for nonsmooth nonconvex optimization

Provides KKT-like optimality conditions for the model

Abstract

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-$L_{0/1}$-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].