论文标题
通过动态稀疏子空间学习，在线结构更改点检测高维流数据
Online Structural Change-point Detection of High-dimensional Streaming Data via Dynamic Sparse Subspace Learning
论文作者
论文摘要
在许多领域，高维流数据变得越来越普遍。它们通常位于多个低维子空间中，由于模式移动或异常发生，歧管结构可能会在时间尺度上突然变化。但是，尚未对实时检测结构变化的问题进行很好的研究。为了填补这一空白，我们提出了一种动态稀疏子空间学习方法，用于在线结构变更点检测高维流数据。提出了一种新型的多个结构变化点模型，并研究了估计量的渐近性能。提出了一种基于贝叶斯信息标准和更改点检测精度的调整方法，以选择罚款系数。提出了有效的修剪精确的基于线性时间的算法，以在线优化和更改点检测。通过几项仿真研究和对运动跟踪手势数据的实际案例研究证明了所提出方法的有效性。
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.
