Reduction of Training Data Using Parallel Hyperplane for Support Vector Machine

Support Vector Machine (SVM) is an efficient machine learning technique applicable to various classification problems due to its robustness. However, its time complexity grows dramatically as the number of training data increases, which makes SVM impractical for large-scale datasets. In this paper, a novel Parallel Hyperplane (PH) scheme is introduced which efficiently omits redundant training data with SVM. In the proposed scheme the PHs are recursively formed while the clusters of data points outside the PHs are removed at each repetition. Computer simulation reveals that the proposed scheme greatly reduces the training time compared to the existing clustering-based reduction scheme and SMO scheme, while allowing the accuracy of classification as high as no data reduction scheme.