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arXiv · 1806.10760

First-order optimal sequential subspace change-point detection

Liyan Xie·George V. Moustakides·Yao Xie
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Abstract

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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Liyan Xie, George V. Moustakides, Yao Xie. 2018-06-28. First-order optimal sequential subspace change-point detection. https://arxiv.org/abs/1806.10760

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