Titre :

A Kernel Multiple Change-point Algorithm via Model Selection

Auteur(s) :

Arlot, Sylvain [Auteur]
Celisse, Alain [Auteur]
Laboratoire Paul Painlevé - UMR 8524
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Harchaoui, Zaid [Auteur]

Titre de la revue :

Journal of Machine Learning Research

Nom court de la revue :

JMLR

Numéro :

20

Pagination :

1-56

Éditeur :

Microtome Publishing

Date de publication :

2019-12

ISSN :

1532-4435

Mot(s)-clé(s) :

Change-point detection
Kernel methods
Model selection
Concentration inequality
Concentration in- equality

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]

Résumé en anglais : [en]

We tackle the change-point problem with data belonging to a general set. We build a penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Cappé (2007). This penalty generalizes the ...
Lire la suite >We tackle the change-point problem with data belonging to a general set. We build a penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Cappé (2007). This penalty generalizes the one proposed by Lebarbier (2005) for one-dimensional signals. We prove a non-asymptotic oracle inequality for the proposed method, thanks to a new concentration result for some function of Hilbert-space valued random variables. Experiments on synthetic data illustrate the accuracy of our method, showing that it can detect changes in the whole distribution of data, even when the mean and variance are constant.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

CNRS
Université de Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2020-06-08T14:10:41Z
2020-06-09T09:03:47Z