Xu, RuiyuWu, JianguoYue, XiaoweiLi, Yongxiang2023-02-072023-02-072022-03-300040-1706http://hdl.handle.net/10919/113700High-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. Supplementary materials for this article are available online.14 page(s)application/pdfenIn CopyrightHigh-dimensional time seriesManifold learningMultiple change-point modelSubspace clusteringGRAPHICAL MODELSALGORITHMSELECTIONNETWORKSArticle - Refereed2023-02-05https://doi.org/10.1080/00401706.2022.2046171Yue, Xiaowei [0000-0001-6019-0940]1537-2723