Online Structural Change-point Detection of High-dimensional Streaming Data via Dynamic Sparse Subspace Learning
This addresses the need for real-time anomaly detection in fields like motion tracking, though it appears incremental as it builds on existing subspace learning methods.
The paper tackles the problem of detecting structural changes in high-dimensional streaming data in real-time, proposing a dynamic sparse subspace learning approach that demonstrates effectiveness through simulation studies and a real case study on gesture data for motion tracking.
High-dimensional streaming data are becoming increasingly ubiquitous in many fields. They often lie in multiple low-dimensional subspaces, and the manifold structures may change abruptly on the time scale due to pattern shift or occurrence of anomalies. However, the problem of detecting the structural changes in a real-time manner has not been well studied. To fill this gap, we propose a dynamic sparse subspace learning approach for online structural change-point detection of high-dimensional streaming data. A novel multiple structural change-point model is proposed and the asymptotic properties of the estimators are investigated. A tuning method based on Bayesian information criterion and change-point detection accuracy is proposed for penalty coefficients selection. An efficient Pruned Exact Linear Time based algorithm is proposed for online optimization and change-point detection. The effectiveness of the proposed method is demonstrated through several simulation studies and a real case study on gesture data for motion tracking.