Single-Pass Distributed Learning of Multi-class SVMs Using Core-Sets

We explore a technique to learn Support Vector Models (SVMs) when training data is partitioned among several data sources. The basic idea is to consider SVMs which can be reduced to Minimal Enclosing Ball (MEB) problems in an feature space. Computation of such SVMs can be efficiently achieved by finding a core-set for the image of the data in the feature space. Our main result is that the union of local core-sets provides a close approximation to a global core-set from which the SVM can be recovered. The method requires hence a single pass through each source of data in order to compute local core-sets and then to recover the SVM from its union. Extensive simulations in small and large datasets are presented in order to evaluate its classification accuracy, transmission efficiency and global complexity, comparing its results with a widely used single-pass heuristic to learn standard SVMs.