Independence clustering (without a matrix)
Document type :
Communication dans un congrès avec actes
Title :
Independence clustering (without a matrix)
Author(s) :
Conference title :
NIPS 2017 - Thirty-first Annual Conference on Neural Information Processing Systems
City :
Long Beach
Country :
Etats-Unis d'Amérique
Start date of the conference :
2017-12-04
HAL domain(s) :
Mathématiques [math]/Théorie de l'information et codage [math.IT]
Statistiques [stat]/Autres [stat.ML]
Informatique [cs]/Apprentissage [cs.LG]
Statistiques [stat]/Autres [stat.ML]
Informatique [cs]/Apprentissage [cs.LG]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :
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- http://arxiv.org/pdf/1703.06700
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- 1703.06700
- Open access
- Access the document