# Nonlinear Kernel Support Vector Machine with 0-1 Soft Margin Loss

arXiv: https://arxiv.org/abs/2203.00399 · HTML: https://mlsanity.com/paper/2203.00399/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems](https://mlsanity.com/paper/1410.1390/) (2014)
- [Structured Nonconvex and Nonsmooth Optimization: Algorithms and Iteration Complexity Analysis](https://mlsanity.com/paper/1605.02408/) (2016)
- [Support Vector Machine Classifier via L0/1 Soft-Margin Loss](https://mlsanity.com/paper/1912.07418/) (2019)
- [Proximal Operator and Optimality Conditions for Ramp Loss SVM](https://mlsanity.com/paper/2011.09059/) (2020)
- [An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines](https://mlsanity.com/paper/1809.03655/) (2018)

## Abstract (arXiv)

Recent advance on linear support vector machine with the 0-1 soft margin loss (L0/1-SVM) shows that the 0-1 loss problem can be solved directly. However, its theoretical and algorithmic requirements restrict us extending the linear solving framework to its nonlinear kernel form directly, the absence of explicit expression of Lagrangian dual function of L0/1-SVM is one big deficiency among of them. In this paper, by applying the nonparametric representation theorem, we propose a nonlinear model for support vector machine with 0-1 soft margin loss, called L0/1-KSVM, which cunningly involves the kernel technique into it and more importantly, follows the success on systematically solving its linear task. Its optimal condition is explored theoretically and a working set selection alternating direction method of multipliers (ADMM) algorithm is introduced to acquire its numerical solution. Moreover, we firstly present a closed-form definition to the support vector (SV) of L0/1-KSVM. Theoretically, we prove that all SVs of L0/1-KSVM are only located on the parallel decision surfaces. The experiment part also shows that L0/1-KSVM has much fewer SVs, simultaneously with a decent predicting accuracy, when comparing to its linear peer L0/1-SVM and the other six nonlinear benchmark SVM classifiers.