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Metric learning is one of the techniques in manifold learning with the goalof finding a projection subspace for increasing and decreasing the inter- andintra-class variances, respectively. Some of the metric learning methods arebased on triplet learning with anchor-positive-negative triplets. Large marginmetric learning for nearest neighbor classification is one of the fundamentalmethods to do this. Recently, Siamese networks have been introduced with thetriplet loss. Many triplet mining methods have been developed for Siamesenetworks; however, these techniques have not been applied on the triplets oflarge margin metric learning for nearest neighbor classification. In this work,inspired by the mining methods for Siamese networks, we propose several tripletmining techniques for large margin metric learning. Moreover, a hierarchicalapproach is proposed, for acceleration and scalability of optimization, wheretriplets are selected by stratified sampling in hierarchical hyper-spheres. Weanalyze the proposed methods on three publicly available datasets, i.e., FisherIris, ORL faces, and MNIST datasets.

Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling