Titre :

Independence clustering (without a matrix)

Auteur(s) :

Ryabko, Daniil [Auteur]
Sequential Learning [SEQUEL]

Titre de la manifestation scientifique :

NIPS 2017 - Thirty-first Annual Conference on Neural Information Processing Systems

Ville :

Long Beach

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2017-12-04

Discipline(s) HAL :

Mathématiques [math]/Théorie de l'information et codage [math.IT]
Statistiques [stat]/Autres [stat.ML]
Informatique [cs]/Apprentissage [cs.LG]

Résumé en anglais : [en]

The independence clustering problem is considered in the following formulation: given a set $S$ of random variables, it is required to find the finest partitioning $\{U_1,\dots,U_k\}$ of $S$ into clusters such that the ...
Lire la suite >The independence clustering problem is considered in the following formulation: given a set $S$ of random variables, it is required to find the finest partitioning $\{U_1,\dots,U_k\}$ of $S$ into clusters such that the clusters $U_1,\dots,U_k$ are mutually independent. Since mutual independence is the target, pairwise similarity measurements are of no use, and thus traditional clustering algorithms are inapplicable. The distribution of the random variables in $S$ is, in general, unknown, but a sample is available. Thus, the problem is cast in terms of time series. Two forms of sampling are considered: i.i.d.\ and stationary time series, with the main emphasis being on the latter, more general, case. A consistent, computationally tractable algorithm for each of the settings is proposed, and a number of open directions for further research are outlined.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL