Smoothed Hinge Loss and $\ell^{1}$ Support Vector Machines

TL;DR

This paper introduces a new efficient algorithm for solving large-scale soft-margin SVMs with an $$ penalty, utilizing smoothing and active set techniques to reduce data passes.

Contribution

It presents a novel algorithm combining smoothing of the hinge loss and active set methods for the  penalty in SVMs, improving scalability.

Findings

Requires fewer passes over data for large datasets

Effective smoothing of hinge loss enhances optimization

Active set approach efficiently handles  penalty

Abstract

A new algorithm is presented for solving the soft-margin Support Vector Machine (SVM) optimization problem with an $\ell^{1}$ penalty. This algorithm is designed to require a modest number of passes over the data, which is an important measure of its cost for very large data sets. The algorithm uses smoothing for the hinge-loss function, and an active set approach for the $\ell^{1}$ penalty.