Totally Deep Support Vector Machines

TL;DR

This paper introduces a novel deep architecture for support vector machines that learns support vectors and kernel parameters simultaneously, enhancing classification performance especially in skeleton-based action recognition tasks.

Contribution

It proposes a deep total variation SVM model that relaxes traditional constraints, enabling support vectors to be learned and combined with kernel parameters for improved accuracy.

Findings

Outperforms baseline methods in skeleton-based action recognition

Learns a wide class of kernels and their combinations

Demonstrates improved generalization over traditional SVMs

Abstract

Support vector machines (SVMs) have been successful in solving many computer vision tasks including image and video category recognition especially for small and mid-scale training problems. The principle of these non-parametric models is to learn hyperplanes that separate data belonging to different classes while maximizing their margins. However, SVMs constrain the learned hyperplanes to lie in the span of support vectors, fixed/taken from training data, and this reduces their representational power and may lead to limited generalization performances. In this paper, we relax this constraint and allow the support vectors to be learned (instead of being fixed/taken from training data) in order to better fit a given classification task. Our approach, referred to as deep total variation support vector machines, is parametric and relies on a novel deep architecture that learns not only the SVM and the kernel parameters but also the support vectors, resulting into highly effective classifiers. We also show (under a particular setting of the activation functions in this deep architecture) that a large class of kernels and their combinations can be learned. Experiments conducted on the challenging task of skeleton-based action recognition show the outperformance of our deep total variation SVMs w.r.t different baselines as well as the related work.