Consistent change-point detection with kernels

2 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
4 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : In this paper we study the kernel change-point algorithm (KCP) proposed by Arlot, Celisse and Harchaoui (2012), 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; iin 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.
Keywords :
Type de document :
Pré-publication, Document de travail
2016
Domaine :

Littérature citée [56 références]

https://hal.archives-ouvertes.fr/hal-01416704
Contributeur : Damien Garreau <>
Soumis le : mercredi 28 juin 2017 - 17:03:30
Dernière modification le : jeudi 26 avril 2018 - 10:29:07
Document(s) archivé(s) le : jeudi 18 janvier 2018 - 02:52:51

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arxiv_submission_20170628.pdf
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• HAL Id : hal-01416704, version 2
• ARXIV : 1612.04740

Citation

Damien Garreau, Sylvain Arlot. Consistent change-point detection with kernels. 2016. 〈hal-01416704v2〉

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