Parallel Semiparametric Support Vector Machines
R. Díaz-Morales, H. Molina-Bulla and Angel Navia-Vazquez
In: Int. Joint Conf. on Neural Networks IJCNN’11, July 31 - August 5, 2011, San Jose, California, USA.

Abstract

In recent years the number of cores in computers has increased considerably, opening new lines of research to adapt classical techniques of machine learning to a parallel scenario. In this paper, we have developed and implemented with the multi-platform application programming interface OpenMP a method to train Semiparametric Support Vector Machines relying on Sparse Greedy Matrix Approximation (SGMA) and Iterated Re-Weighted Least Squares algorithm (IRWLS). We take advantage of the matrix formulation of SGMA and IRWLS. We recursively apply the partitioned matrix inversion lemma and other matrix decompositions to obtain a simple procedure to parallelize SVMS with good performance and computational efficiency.

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