Anomaly Localization for Network Data Streams With Graph Joint Sparse PCA
Determining anomalies in data streams that are collected and transformed from various types of networks has recently attracted significant research interest. Principal Component Analysis (PCA) has been extensively applied to detecting anomalies in network data streams. However, none of existing PCA based approaches addresses the problem of identifying the sources that contribute most to the observed anomaly, or anomaly localization. In this paper, the authors propose novel sparse PCA methods to perform anomaly detection and localization for network data streams. Their key observation is that they can localize anomalies by identifying a sparse low dimensional space that captures the abnormal events in data streams.