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2018 Consistent change-point detection with kernels
Damien Garreau, Sylvain Arlot
Electron. J. Statist. 12(2): 4440-4486 (2018). DOI: 10.1214/18-EJS1513


In this paper we study the kernel change-point algorithm (KCP) proposed by Arlot, Celisse and Harchaoui [5], which aims at locating an unknown number of change-points in the distribution of a sequence of independent data taking values in an arbitrary set. The change-points are selected by model selection with a penalized kernel empirical criterion. We provide a non-asymptotic result showing that, with high probability, the KCP procedure retrieves the correct number of change-points, provided that the constant in the penalty is well-chosen; in addition, KCP estimates the change-points location at the optimal rate. As a consequence, when using a characteristic kernel, KCP detects all kinds of change in the distribution (not only changes in the mean or the variance), and it is able to do so for complex structured data (not necessarily in $\mathbb{R}^{d}$). Most of the analysis is conducted assuming that the kernel is bounded; part of the results can be extended when we only assume a finite second-order moment. We also demonstrate KCP on both synthetic and real data.


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Damien Garreau. Sylvain Arlot. "Consistent change-point detection with kernels." Electron. J. Statist. 12 (2) 4440 - 4486, 2018.


Received: 1 June 2017; Published: 2018
First available in Project Euclid: 18 December 2018

zbMATH: 07003248
MathSciNet: MR3892345
Digital Object Identifier: 10.1214/18-EJS1513

Primary: 62M10
Secondary: 62G20

Keywords: change-point detection , kernel methods , penalized least-squares


Vol.12 • No. 2 • 2018
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