Local Deep Kernel Learning for Efficient Non-Linear SVM Prediction

Abstract

Our objective is to speed up non-linear SVM prediction while maintaining classification accuracy above an acceptable limit. We generalize Localized Multiple Kernel Learning so as to learn a primal feature space embedding which is high dimensional, sparse and computationally deep. Primal based classification decouples prediction costs from the number of support vectors and our tree-structured features efficiently encode non-linearities while speeding up prediction exponentially over the state-of-the-art. We develop routines for optimizing over the space of tree-structured features and efficiently scale to problems with over half a million training points. Experiments on benchmark data sets reveal that our formulation can reduce prediction costs by more than three orders of magnitude in some cases with a moderate sacrifice in classification accuracy as compared to RBF-SVMs. Furthermore, our formulation leads to much better classification accuracies over leading methods.