An efficient SEM algorithm for Gaussian ...
Document type :
Communication dans un congrès avec actes
Title :
An efficient SEM algorithm for Gaussian Mixtures with missing data
Author(s) :
Vandewalle, Vincent [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
MOdel for Data Analysis and Learning [MODAL]
Biernacki, C [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]

Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
MOdel for Data Analysis and Learning [MODAL]
Biernacki, C [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Conference title :
8th International Conference of the ERCIM WG on Computational and Methodological Statistics
City :
Londres
Country :
Royaume-Uni
Start date of the conference :
2015-12-12
Publication date :
2015-12-12
HAL domain(s) :
Mathématiques [math]
Mathématiques [math]/Statistiques [math.ST]
Mathématiques [math]/Statistiques [math.ST]
English abstract : [en]
The missing data problem is well-known for statisticians but its frequency increases with the growing size of modern datasets. In Gaussian model-based clustering, the EM algorithm easily takes into account such data by ...
Show more >The missing data problem is well-known for statisticians but its frequency increases with the growing size of modern datasets. In Gaussian model-based clustering, the EM algorithm easily takes into account such data by dealing with two kinds of latent levels: the components and the variables. However, the quite familiar degeneracy problem in Gaussian mixtures is aggravated during the EM runs. Indeed, numerical experiments clearly reveal that degeneracy is quite slow and also more frequent than with complete data. In practice, such situations are difficult to detect efficiently. Consequently, degenerated solutions may be confused with valuable solutions and, in addition, computing time may be wasted through wrong runs. A theoretical and practical study of the degeneracy will be presented. Moreover a simple condition on the latent partition to avoid degeneracy will be exhibited. This condition is used in a constrained version of the Stochastic EM (SEM) algorithm. Numerical experiments on real and simulated data illustrate the good behaviour of the proposed algorithm.Show less >
Show more >The missing data problem is well-known for statisticians but its frequency increases with the growing size of modern datasets. In Gaussian model-based clustering, the EM algorithm easily takes into account such data by dealing with two kinds of latent levels: the components and the variables. However, the quite familiar degeneracy problem in Gaussian mixtures is aggravated during the EM runs. Indeed, numerical experiments clearly reveal that degeneracy is quite slow and also more frequent than with complete data. In practice, such situations are difficult to detect efficiently. Consequently, degenerated solutions may be confused with valuable solutions and, in addition, computing time may be wasted through wrong runs. A theoretical and practical study of the degeneracy will be presented. Moreover a simple condition on the latent partition to avoid degeneracy will be exhibited. This condition is used in a constrained version of the Stochastic EM (SEM) algorithm. Numerical experiments on real and simulated data illustrate the good behaviour of the proposed algorithm.Show less >
Language :
Anglais
Peer reviewed article :
Non
Audience :
Internationale
Popular science :
Non
Collections :
Source :
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