A local mixture based SVM for an efficient supervised binary classification

Despite support vector machines' (SVM) robustness and optimality, SVM do not scale well computationally. Suffering from slow training convergence on large datasets, SVM online testing time can be suboptimal because SVM write the classifier hyper-plane model as a sum of support vectors that could total as much as half the datasets. Motivated to speed up SVM real time testing by reducing the number of SV, we introduce in this paper a novel local mixture based SVM (LMSVM) approach that exploits the increased separability provided by the kernel trick, while introducing a onetime computational expense. LMSVM applies kernel k-means clustering to the data in kernel space before pruning unwanted clusters based on a mixture measure for label heterogeneity. LMSVM's computational complexity and classification accuracy on four databases from UCI show promising results and motivate follow on research.