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.
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.