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

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. Supplementary materials for this article are available online

Ketersediaan

Informasi Detail

Judul Seri

TECHNOMETRICS; Vol.65 No.1 February 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 19-32

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MANIFOLD LEARNING
DYNAMIC LEARNING
SUBSPACE CLUSTERING
HIGH-DIMENSIONAL TIME SERIES
MULTIPLE CHANGE-POINT MODEL

Info Detail Spesifik

https://doi.org/10.1080/00401706.2022.2046171

Pernyataan Tanggungjawab

Ruiyu Xu, Jianguo Wu, Xiaowei Yue, Yongxiang Li

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