A boosting approach for supervised Mahalanobis distance metric learning

Abstract

Determining a proper distance metric is often a crucial step for machine learning. In this paper, a boosting algorithm is proposed to learn a Mahalanobis distance metric. Similar to most boosting algorithms, the proposed algorithm improves a loss function iteratively. In particular, the loss function is defined in terms of hypothesis margins, and a metric matrix base-learner specific to the boosting framework is also proposed. Experimental results show that the proposed approach can yield effective Mahalanobis distance metrics for a variety of data sets, and demonstrate the feasibility of the proposed approach.