标题： 基于张量分解的鲁棒时空连续异常检测 显示英文标题

标题： Robust Spatiotemporally Contiguous Anomaly Detection Using Tensor Decomposition

摘要： 在时空数据中的异常检测是一个在各种应用中遇到的具有挑战性的问题，包括视频监控、医学影像数据和城市交通监控。 现有的异常检测方法主要关注点异常，无法处理时空数据中出现的时间和空间依赖性。 基于张量的异常检测方法已被提出以解决这个问题。 尽管现有方法可以捕捉不同模式之间的依赖性，但它们主要是监督的，并未考虑异常的具体结构。 此外，这些方法主要关注于提取异常特征，而没有提供任何统计置信度。 在本文中，我们引入了一种无监督的基于张量的异常检测方法，该方法同时考虑了异常的稀疏性和时空平滑性。 将异常检测问题表述为一个正则化鲁棒低秩+稀疏张量分解，其中张量相对于底层空间和时间图的总变差量化了异常的时空平滑性。 一旦提取了异常特征，我们引入了一个统计异常评分框架，该框架考虑了局部时空依赖性。 所提出的框架在合成数据和真实数据上进行了评估。 显示英文摘要

摘要： Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications, including video surveillance, medical imaging data, and urban traffic monitoring. Existing anomaly detection methods focus mainly on point anomalies and cannot deal with temporal and spatial dependencies that arise in spatio-temporal data. Tensor-based anomaly detection methods have been proposed to address this problem. Although existing methods can capture dependencies across different modes, they are primarily supervised and do not account for the specific structure of anomalies. Moreover, these methods focus mainly on extracting anomalous features without providing any statistical confidence. In this paper, we introduce an unsupervised tensor-based anomaly detection method that simultaneously considers the sparse and spatiotemporally smooth nature of anomalies. The anomaly detection problem is formulated as a regularized robust low-rank + sparse tensor decomposition where the total variation of the tensor with respect to the underlying spatial and temporal graphs quantifies the spatiotemporal smoothness of the anomalies. Once the anomalous features are extracted, we introduce a statistical anomaly scoring framework that accounts for local spatio-temporal dependencies. The proposed framework is evaluated on both synthetic and real data.