Accelerating the Training Process of Support Vector Machines by Random Partition

—In this paper we present a novel method, Random Partition based SVM (RPSVM), for speeding up SVM training. Instead of clustering the training data prior to training, RPSVM randomly partitions the training data into several clusters and then uses the centers of the clusters to train an initial SVM. This trained SVM is used to find critical clusters which are located on the decision boundary. The same procedure is applied repeatedly to each of the critical clusters, resulting in a refined SVM which consists of the supporting vectors in the initial round of training and those in the repeated round. This procedure is repeated recursively until no critical cluster exists, resulting in the final SVM. Our experiments on synthetic and real data sets have shown that RPSVM is indeed scalable to large data sets while the high performance is retained.