  Metric learning is one of the techniques in manifold learning with the goal
of finding a projection subspace for increasing and decreasing the inter- and
intra-class variances, respectively. Some of the metric learning methods are
based on triplet learning with anchor-positive-negative triplets. Large margin
metric learning for nearest neighbor classification is one of the fundamental
methods to do this. Recently, Siamese networks have been introduced with the
triplet loss. Many triplet mining methods have been developed for Siamese
networks; however, these techniques have not been applied on the triplets of
large margin metric learning for nearest neighbor classification. In this work,
inspired by the mining methods for Siamese networks, we propose several triplet
mining techniques for large margin metric learning. Moreover, a hierarchical
approach is proposed, for acceleration and scalability of optimization, where
triplets are selected by stratified sampling in hierarchical hyper-spheres. We
analyze the proposed methods on three publicly available datasets, i.e., Fisher
Iris, ORL faces, and MNIST datasets.
