论文标题
亲和力指导的几何半监督指标学习
Affinity guided Geometric Semi-Supervised Metric Learning
论文作者
论文摘要
在本文中，我们改造了被遗忘的经典半监督距离度量学习（SSDML）问题，从riemannian几何镜头，以利用端到端深层框架内的随机优化。动机源于以下事实：除了一些经典的SSDML方法学习线性摩alanobis指标外，还没有研究深SSDML。我们首先将现有的SSDML方法扩展到其深度对应物，然后提出一种克服其局限性的新方法。由于对我们的度量参数的约束性质，我们利用Riemannian优化。我们具有新颖的基于亲和力传播的三胞胎挖掘策略的深层SSDML方法优于其竞争对手。
In this paper, we revamp the forgotten classical Semi-Supervised Distance Metric Learning (SSDML) problem from a Riemannian geometric lens, to leverage stochastic optimization within a end-to-end deep framework. The motivation comes from the fact that apart from a few classical SSDML approaches learning a linear Mahalanobis metric, deep SSDML has not been studied. We first extend existing SSDML methods to their deep counterparts and then propose a new method to overcome their limitations. Due to the nature of constraints on our metric parameters, we leverage Riemannian optimization. Our deep SSDML method with a novel affinity propagation based triplet mining strategy outperforms its competitors.
