  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.
