论文标题
通过跨透明拷贝最大化的更改点检测
Change Point Detection by Cross-Entropy Maximization
论文作者
论文摘要
许多离线无监督的变更点检测算法依赖于最大程度地减少细分成本的惩罚总和。我们通过提议最大程度地减少各个细分之间的差异来扩展此框架。特别是，我们建议选择变更点，以最大程度地提高连续细分之间的跨渗透性，并通过引入新变更点的惩罚来平衡。我们提出了一种动态编程算法来解决此问题并分析其复杂性。与三种最先进的方法相比，两个具有挑战性的数据集的实验证明了我们方法的优势。
Many offline unsupervised change point detection algorithms rely on minimizing a penalized sum of segment-wise costs. We extend this framework by proposing to minimize a sum of discrepancies between segments. In particular, we propose to select the change points so as to maximize the cross-entropy between successive segments, balanced by a penalty for introducing new change points. We propose a dynamic programming algorithm to solve this problem and analyze its complexity. Experiments on two challenging datasets demonstrate the advantages of our method compared to three state-of-the-art approaches.
