Efficient Max-Margin Metric Learning

Efficient learning of an appropriate distance metric is an increasingly important problem in machine learning. However, current methods are limited by scalability issues or are unsuited to use with general similarity/dissimilarity constraints. In this paper, we propose an efficient metric learning method based on the max-margin framework with pairwise constraints that has a strong generalization guarantee. First, we reformulate the max-margin metric learning problem as a structured support vector machine which we can optimize in linear time via a cutting-plane method. Second, we propose a kernelized extention and an approximation method based on matching pursuit that allows linear-time training even in the kernel case.We find our method to be comparable to or better than state of the art metric learning techniques at a number of machine learning and computer vision classification tasks.