Totally Deep Support Vector Machines

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.

References in corpus (10)

• Training Very Deep Networks
• Simplifying Graph Convolutional Networks
• Deep Learning using Linear Support Vector Machines
• An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
• Graph Neural Networks with convolutional ARMA filters
• Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
• Support Vector Machines under Adversarial Label Contamination
• Convolutional Kernel Networks
• Pose-conditioned Spatio-Temporal Attention for Human Action Recognition
• Deep Multiple Kernel Learning