论文标题
学习sindhorn分歧以进行监督的变更点检测
Learning Sinkhorn divergences for supervised change point detection
论文作者
论文摘要
许多现代应用需要检测复杂顺序数据中的变更点。大多数现有的变更点检测方法都是无监督的，因此缺乏有关我们要检测到的更改或某种更改是否安全忽略的任何信息。这通常会导致变化检测性能不佳。我们提出了一个新颖的变更点检测框架，该框架使用真实的变更点实例作为学习地面度量的监督，以便可以在滑动窗口的两样本测试中使用sindhorn差异，以在线方式检测变更点。我们的方法可用于学习稀疏度量，该度量对于高维更改点检测设置中的特征选择和解释都有用。对模拟以及现实世界序列进行的实验表明，我们提出的方法可以基本上改善更改点检测性能，而不是仅使用几乎没有标记的变更点实例的现有无监督变更点检测方法。
Many modern applications require detecting change points in complex sequential data. Most existing methods for change point detection are unsupervised and, as a consequence, lack any information regarding what kind of changes we want to detect or if some kinds of changes are safe to ignore. This often results in poor change detection performance. We present a novel change point detection framework that uses true change point instances as supervision for learning a ground metric such that Sinkhorn divergences can be then used in two-sample tests on sliding windows to detect change points in an online manner. Our method can be used to learn a sparse metric which can be useful for both feature selection and interpretation in high-dimensional change point detection settings. Experiments on simulated as well as real world sequences show that our proposed method can substantially improve change point detection performance over existing unsupervised change point detection methods using only few labeled change point instances.
