论文标题
积极的半决赛支持矢量回归度量度学习
Positive semidefinite support vector regression metric learning
论文作者
论文摘要
大多数现有的度量学习方法都集中在学习依靠样本对之间的相似和不同关系的相似性或距离测量。但是，在许多现实世界应用中，例如多标签学习，标签分布学习。为此，提出了关系一致性度量学习（RAML）框架来处理这些情况下的度量学习问题。但是RAML框架使用SVR求解器进行优化。它无法学习积极的半决赛距离度量公制，这是公制学习中必不可少的。在本文中，我们提出了两种甲基，以克服弱点。此外，我们进行了几项有关单标签分类，多标签分类，标签分布学习的实验，以证明新方法可实现针对RAML框架的有利性能。
Most existing metric learning methods focus on learning a similarity or distance measure relying on similar and dissimilar relations between sample pairs. However, pairs of samples cannot be simply identified as similar or dissimilar in many real-world applications, e.g., multi-label learning, label distribution learning. To this end, relation alignment metric learning (RAML) framework is proposed to handle the metric learning problem in those scenarios. But RAML framework uses SVR solvers for optimization. It can't learn positive semidefinite distance metric which is necessary in metric learning. In this paper, we propose two methds to overcame the weakness. Further, We carry out several experiments on the single-label classification, multi-label classification, label distribution learning to demonstrate the new methods achieves favorable performance against RAML framework.
