Mean SVM for large scale visual classication

PmSVM (Power Mean SVM), a classifier that trains sig-nificantly faster than state-of-the-art linear and non-linear SVM solvers in large scale visual classification tasks, is pre-sented. PmSVM also achieves higher accuracies. A scal-able learning method for large vision problems, e.g., with millions of examples or dimensions, is a key component in many current vision systems. Recent progresses have enabled linear classifiers to efficiently process such large scale problems. Linear classifiers, however, usually have inferior accuracies in vision tasks. Non-linear classifiers, on the other hand, may take weeks or even years to train. We propose a power mean kernel and present an efficient learn-ing algorithm through gradient approximation. The power mean kernel family include as special cases many popular additive kernels. Empirically, PmSVM is up to 5 times faster than LIBLINEAR, and two times faster than state-of-the-art additive kernel classifiers. In terms of accuracy, it outper-forms state-of-the-art additive kernel implementations, and has major advantages over linear SVM. 1