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

TL;DR

This paper introduces a real-time method for detecting structural change-points in high-dimensional streaming data by leveraging dynamic sparse subspace learning, addressing a gap in online change detection research.

Contribution

It proposes a novel multiple change-point model with asymptotic analysis, a BIC-based tuning method, and an efficient algorithm for online detection in high-dimensional data.

Findings

Effective in simulation studies

Successfully applied to gesture motion tracking data

Outperforms existing methods in accuracy and speed

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.