Anomaly Detection

Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling

Tensor clustering has become an important topic, specifically in spatio-temporal modeling, due to its ability to cluster spatial modes (e.g., stations or road segments) and temporal modes (e.g., time of the day or day of the week). Our motivating …

Sparse Decomposition Methods for Spatio-Temporal Anomaly Detection

Anomaly detection constitutes a critical field of research, concerned with the identification of rare, atypical, or unexpected patterns within a dataset. Within the existing literature, the majority of anomaly detection techniques lack the capability …

Profile Decomposition based Hybrid Transfer Learning for Cold-start Data Anomaly Detection

Anomaly detection is an essential task for quality management in smart manufacturing. An accurate data-driven detection method usually needs enough data and labels. However, in practice, there commonly exist newly set-up processes in manufacturing, …