An accelerator for support vector machines based on the local geometrical information and data partition

The support vector machines (SVM) is difficult to deal with large datasets for its low training efficiency. One of the important solutions has been developed by dividing a whole dataset into smaller subsets with data partition and combining the results of the classifiers over the divided subsets. However, traditional data partition approaches are difficult to preserve the class boundary of the dataset or control the size of divided subsets, so that their performance will be greatly influenced. To overcome this difficulty, we propose an accelerator for SVM algorithm based on the local geometrical information. In this algorithm, the feature space is divided into several regions with the approximately equal number of training instances by linear projection, and then each SVM classifier trained over the extended region only predicts the unlabeled instances within that original region. The proposed algorithm can not only hold the decision boundary of the raw data, but also saves a lot of execution time for implementing it in a parallel environment. Furthermore, the number of instances within each divided regions can be effectively controlled; it is conducive to choose the complexity of the execution in each of the processors. Experiments show that the classification performance of the proposed algorithm compares favorably with four state-of-the-art algorithms with the least training time.