Comparative Evaluation of Batch and Online Distance Metric Learning Approaches Based on Margin Maximization

Distance metric learning aims at obtaining an appropriate metric that conveniently adapts to a particular recognition problem given a set of training pairs. The idea of maximizing a margin that separates similar and dissimilar objects has been used in different ways in several recent works. This paper considers two different learning schemes aiming at the same goal but posing the learning problem either as a batch or as an online formulation. Extensive experiments and the corresponding discussion try to put forward the advantages and drawbacks of each of the approaches considered.