论文标题
使用三重挖掘和分层抽样的最接近邻居分类的大幅度度量学习的加速度加速
Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
论文作者
论文摘要
公制学习是多种学习中的技术之一，其目的是找到一个投影子空间，以分别增加和减少阶段和内部方差。某些公制学习方法基于具有锚阳性阴性三胞胎的三胞胎学习。最接近邻居分类的大量保证金度量是这样做的基本方法之一。最近，引入了三胞胎损失的暹罗网络。已经为暹罗网络开发了许多三胞胎挖掘方法。但是，这些技术尚未应用于最接近邻居分类的大幅度度量学习的三胞胎。在这项工作中，受暹罗网络的采矿方法的启发，我们提出了几种三胞胎挖掘技术，以用于大量保证金度量。此外，提出了一种层次方法，以加速和优化的可扩展性，其中三联体是通过分层的分层抽样选择的三元组。我们分析了三个公开可用数据集的提议方法，即Fisher Iris，Orl面和MNIST数据集。
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.
