Graph-Based Change-Point Analysis
Graph-Based Change-Point Analysis
复制标题
基于图的变点分析
DOI：
10.1146/annurev-statistics-122121-033817
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发表时间：
2023-03
影响因子：
7.9
通讯作者：
Hao Chen;Lynna Chu
中科院分区：
文献类型：
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作者：
Hao Chen;Lynna Chu
最近的技术进步允许收集大量数据，以研究不同领域随时间和/或空间的复杂现象。其中许多数据涉及高维或非欧几里得测量序列，其中变点分析是理解数据的关键早期步骤。分割，或离线变化点分析，将数据划分为均匀的时间或空间段，使后续分析更容易；它的在线对应物检测顺序观察数据的变化，允许实时异常检测。本文回顾了一个非参数变化点分析框架，该框架利用图形表示观测值之间的相似性。只要可以定义观测值之间合理的不相似距离，该框架就可以应用于数据。因此，该框架可以应用于广泛的应用，从高维数据到非欧几里德数据，如成像数据或网络数据。此外，可以推导出分析公式来控制错误发现，使其成为现成的数据分析工具。
Recent technological advances allow for the collection of massive data in the study of complex phenomena over time and/or space in various fields. Many of these data involve sequences of high-dimensional or non-Euclidean measurements, where change-point analysis is a crucial early step in understanding the data. Segmentation, or offline change-point analysis, divides data into homogeneous temporal or spatial segments, making subsequent analysis easier; its online counterpart detects changes in sequentially observed data, allowing for real-time anomaly detection. This article reviews a nonparametric change-point analysis framework that utilizes graphs representing the similarity between observations. This framework can be applied to data as long as a reasonable dissimilarity distance among the observations can be defined. Thus, this framework can be applied to a wide range of applications, from high-dimensional data to non-Euclidean data, such as imaging data or network data. In addition, analytic formulas can be derived to control the false discoveries, making them easy off-the-shelf data analysis tools.