Non-linear classification of massive datasets with a parallel algorithm of local support vector machines

Abstract

We propose a new parallel algorithm of local support vector machines, called kSVM for the effectively non-linear classification of large datasets. The learning strategy of kSVM uses kmeans algorithm to partition the data into k clusters, followed which it constructs a non-linear SVMin each cluster to classify the data locally in the parallel way on multi-core computers. The kSVM algorithm is faster than the standard SVM in the non-linear classification of large datasets while maintaining the classification correstness. The numerical test results on 4 datasets from UCI repository and 3 benchmarks of handwritten letters recognition showed that our proposal is efficient compared to the standard SVM.