Metric learning by directly minimizing the k-NN training error

This paper presents an approach for computing global distance metrics that minimize the k-NN leave-one-out (LOO) error. The approach optimizes an energy function that corresponds to a smoothened version of the k-NN LOO error. The generalization of the proposed approach is further improved by controlling the k parameter through a heuristic. Evaluation of the proposed approach on several public datasets showed that it was able to compete with an established state-of-the art approach.