A Dual Coordinate Descent Method For Large-scale Linear SVM

Abstract

In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such large-scale sparse data. This paper presents a novel dual coordinate descent method for linear SVM with L1-and L2-loss functions. The proposed method is simple and reaches an <i>ε</i>-accurate solution in <i>O</i>(log(1/<i>ε</i>)) iterations. Experiments indicate that our method is much faster than state of the art solvers such as Pegasos, TRON, SVM^perf, and a recent primal coordinate descent implementation.