Parallel algorithm for training multiclass proximal Support Vector Machines

In this paper we describe a proximal Support Vector Machine algorithm for multiclassification problem by one-vs-all scheme. The computational requirement for the new algorithm is almost the same as training one of its element binary proximal Support Vector Machines. Low rank approximation is taken to reduce computational costs when the kernel matrix is too large. An error bound estimation for the approximated solution is given, which is used as a stopping criteria for low rank approximation. A post-processing strategy is developed to overcome the difficulty arising from unbalanced data and to improve the classification accuracy. A parallel implementation of the algorithm using standard MPI communication routines is provided to handle large-scale problems and to accelerate the training process. Experiment results on several public datasets validate the effectiveness of our proposed algorithm.