论文标题
光泽：时空城市交通数据中基于张量的异常检测
GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data
论文作者
论文摘要
时空数据中的异常检测是在多种应用中遇到的一个具有挑战性的问题，包括高光谱成像，视频监视和城市交通监测。就城市交通数据而言，异常是指不寻常的事件，例如交通拥堵和意外的人群聚会。由于异常定义对时间和空间的依赖性，检测这些异常是具有挑战性的。在本文中，我们介绍了一种基于张量的无张量的异常检测方法，用于时空城市交通数据。提出的方法假设异常是稀疏且暂时连续的，{i.e.}，异常在空间连续的位置组中显示出，这些位置的位置组始终显示出异常值，这些位置在短时间内始终如一地显示出异常的值。此外，采用了一种多种嵌入方法来在每种模式下保留数据的局部几何结构。所提出的框架，图形正规化的低级别以及暂时平滑的稀疏分解（GLOSS）作为优化问题，并使用乘数交替方法（ADMM）解决。结果显示，所得算法会融合并与缺失的数据和噪声保持稳定。对合成和实际时空城市交通数据进行了评估，并与基线方法进行了评估。
Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance and urban traffic monitoring. In the case of urban traffic data, anomalies refer to unusual events such as traffic congestion and unexpected crowd gatherings. Detecting these anomalies is challenging due to the dependence of anomaly definition on time and space. In this paper, we introduce an unsupervised tensor-based anomaly detection method for spatiotemporal urban traffic data. The proposed method assumes that the anomalies are sparse and temporally continuous, {i.e.}, anomalies appear as spatially contiguous groups of locations that show anomalous values consistently for a short duration of time. Furthermore, a manifold embedding approach is adopted to preserve the local geometric structure of the data across each mode. The proposed framework, Graph Regularized Low-rank plus Temporally Smooth Sparse decomposition (GLOSS), is formulated as an optimization problem and solved using alternating method of multipliers (ADMM). The resulting algorithm is shown to converge and be robust against missing data and noise. The proposed framework is evaluated on both synthetic and real spatiotemporal urban traffic data and compared with baseline methods.
