论文标题
序数：取回序数时间序列中缺失的类
Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series
论文作者
论文摘要
在本文中，我们提出了一个有序的时间序列分类框架，该框架可与培训数据中缺少的课程（即在测试期间我们可以开处方培训期间缺少的课程）有力。该框架依赖于两个主要组成部分：（1）我们新提出的序数量损失，这迫使模型学习潜在表示，同时保留标签之间的顺序关系，（2）测试程序，该程序利用了潜在表示的属性（顺序保存）。我们根据现实世界多元时间序列数据进行实验，并显示了缺少标签的预测的显着改善，即使培训中缺少40％的班级。与众所周知的三胞胎损失优化相比，插值增加了信息，在某些情况下，我们的精度几乎翻了一番。
In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i.e., during testing we can prescribe classes that are missing during training. This framework relies on two main components: (1) our newly proposed ordinal-quadruplet loss, which forces the model to learn latent representation while preserving the ordinal relation among labels, (2) testing procedure, which utilizes the property of latent representation (order preservation). We conduct experiments based on real world multivariate time series data and show the significant improvement in the prediction of missing labels even with 40% of the classes are missing from training. Compared with the well-known triplet loss optimization augmented with interpolation for missing information, in some cases, we nearly double the accuracy.
