A Dual Coordinate Descent Method for Large-scale Linear SVM

author: Kai-Wei Chang, Department of Computer Science and Information Engineering, National Taiwan University
published: Aug. 5, 2008,   recorded: July 2008,   views: 6603


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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 epsilon-accurate solution in O(log (1/epsilon)) iterations. Experiments indicate that our method is much faster than state of the art solvers such as Pegasos, Tron, svmperf, and a recent primal coordinate descent implementation.

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