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First-order optimal sequential subspace change-point detection

Citation Author(s):
Liyan Xie, George V. Moustakides, Yao Xie
Submitted by:
Liyan Xie
Last updated:
21 November 2018 - 7:42pm
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters:
Liyan Xie
Paper Code:
1206
 

We consider the sequential change-point detection problem of detecting changes that are characterized by a subspace structure. Such changes are frequent in high-dimensional streaming data altering the form of the corresponding covariance matrix. In this work we present a Subspace-CUSUM procedure and demonstrate its first-order asymptotic optimality properties for the case where the subspace structure is unknown and needs to be simultaneously estimated. To achieve this goal we develop a suitable analytical methodology that includes a proper parameter optimization for the proposed detection scheme. Numerical simulations corroborate our theoretical findings.

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