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

TL;DR

This paper introduces novel triplet mining and stratified sampling techniques to accelerate large margin metric learning for nearest neighbor classification, improving scalability and efficiency.

Contribution

It adapts triplet mining methods from Siamese networks to large margin metric learning and proposes a hierarchical stratified sampling approach for faster optimization.

Findings

Improved training speed and scalability on benchmark datasets

Enhanced accuracy in nearest neighbor classification

Effective triplet selection strategies for metric learning

Abstract

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.