高维在线变点检测：自适应阈值与可解释性
High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability
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中文总结 AI 辅助
本文提出基于切片Wasserstein距离的高维在线变点检测框架，结合自适应阈值和对比解释，显著降低误报率并提升可解释性。
中文摘要 AI 辅助
变点检测（CPD）用于识别序列数据中突然且显著的变化，其应用涵盖人类活动识别、金融市场、网络安全、制造业和自主系统。传统CPD方法在高维场景下常面临计算挑战，且通常对检测到的变化提供的解释有限，这限制了其实际可用性。本文提出一种CPD框架，通过利用切片Wasserstein（SW）距离来提高可扩展性和可解释性。我们的贡献有四个方面：（1）我们使用SW距离将多元序列数据转换为一维分数，使所得表示与现有CPD方法兼容；（2）我们分析了SW距离随机切片的分布行为，并表明在适当假设下，它们可近似为Gamma分布，为阈值校准提供了原则性基础；（3）我们提出一种自适应在线CPD算法，将基于SW的分数与自适应分位数阈值相结合；（4）我们引入一个模型特定的框架，用于为标注的变点生成对比性解释。实验上，与流行的在线和离线CPD基线相比，我们的方法平均将误报率降低至少48%，同时保持有竞争力或更优的检测性能。代码可在该https URL获取。同时，它产生可解释的变点标注，使其适用于高风险应用中的部署。
英文摘要
Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial markets, cybersecurity, manufacturing, and autonomous systems. Traditional CPD methods often face computational challenges in high-dimensional settings and typically provide limited explanations for detected changes, which can restrict their practical usability. This paper introduces a CPD framework that improves scalability and interpretability by leveraging the Sliced Wasserstein (SW) distance. Our contributions are fourfold: (1) we transform multivariate sequential data into one-dimensional scores using the SW distance, making the resulting representation compatible with existing CPD methods; (2) we analyze the distributional behavior of random slices of the SW distance and show that, under suitable assumptions, they can be approximated by a Gamma distribution, providing a principled basis for threshold calibration; (3) we propose a self-adapting online CPD algorithm that combines this SW-based score with an adaptive quantile-based threshold; (4) we introduce a model-specific framework for generating contrastive explanations for annotated change points. Empirically, our method reduces false positives by at least $48\%$ on average compared with popular online and offline CPD baselines, while maintaining competitive or superior detection performance. Code is available at https://github.com/jsve96/SWCPD_Code. At the same time, it produces interpretable change-point annotations, making it practical for deployment in high-stakes applications.
发表机构
- Federal Institute for Occupational Safety and Health, Dresden（德累斯顿联邦职业安全与健康研究所）
- Technical University of Munich（慕尼黑工业大学）
- Munich Center for Machine Learning（慕尼黑机器学习中心）
- Sapienza University of Rome（罗马第一大学）
机构由 AI 辅助整理，请以论文原文为准。
