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arXiv · 1512.07716

Fast Parallel SVM using Data Augmentation

Hugh Perkins·Minjie Xu·Jun Zhu·Bo Zhang
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Abstract

As one of the most popular classifiers, linear SVMs still have challenges in dealing with very large-scale problems, even though linear or sub-linear algorithms have been developed recently on single machines. Parallel computing methods have been developed for learning large-scale SVMs. However, existing methods rely on solving local sub-optimization problems. In this paper, we develop a novel parallel algorithm for learning large-scale linear SVM. Our approach is based on a data augmentation equivalent formulation, which casts the problem of learning SVM as a Bayesian inference problem, for which we can develop very efficient parallel sampling methods. We provide empirical results for this parallel sampling SVM, and provide extensions for SVR, non-linear kernels, and provide a parallel implementation of the Crammer and Singer model. This approach is very promising in its own right, and further is a very useful technique to parallelize a broader family of general maximum-margin models.

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Hugh Perkins, Minjie Xu, Jun Zhu, Bo Zhang. 2015-12-24. Fast Parallel SVM using Data Augmentation. https://arxiv.org/abs/1512.07716

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