PmSVM (Power Mean SVM), a classifier that trains significantly faster than state-of-the-art linear and non-linear
SVM solvers in large scale visual classification tasks, is presented. PmSVM also achieves higher accuracies. A scalable 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 learning 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 outperforms state-of-the-art additive kernel implementations, and
has major advantages over linear SVM.
