Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
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Date
2021-01-15
Authors
Poorhevari, Parisa Abdolrahim
Ghojogh, Benyamin
Gaudet, Vincent C.
Karray, Fakhri
Crowley, Mark
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Metric learning is a technique in manifold learning to find a pro- jection subspace for increasing and decreasing the inter- and intra-class variances, respectively. Some 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 nets; however, these techniques have not been applied on the triplets of large mar- gin metric learning. In this work, inspired by the mining methods for Siamese nets, 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 avail- able datasets.
Description
This article is published by the Journal of Computational Vision and Imaging Systems, available here: https://doi.org/10.15353/jcvis.v6i1.3534. This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
Keywords
data mining, classification