Online Structural Change-point Detection of High-dimensional Streaming Data via Dynamic Sparse Subspace Learning

• URL: http://arxiv.org/abs/2009.11713v3
• Date: Tue, 12 Apr 2022 05:27:40 GMT
• Title: Online Structural Change-point Detection of High-dimensional Streaming Data via Dynamic Sparse Subspace Learning
• Authors: Ruiyu Xu, Jianguo Wu, Xiaowei Yue and Yongxiang Li
• Abstract summary: 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 properties of the estimators are investigated. An efficient Pruned Exact Linear Time based algorithm is proposed for online optimization and change-point detection.
• Score: 9.050841801109332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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