Probabilistic Auto-Associative Models and ...
Type de document :
Article dans une revue scientifique
URL permanente :
Titre :
Probabilistic Auto-Associative Models and Semi-Linear PCA
Auteur(s) :
Iovleff, Serge [Auteur]
Laboratoire Paul Painlevé - UMR 8524
Laboratoire Paul Painlevé - UMR 8524
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Laboratoire Paul Painlevé - UMR 8524
Laboratoire Paul Painlevé - UMR 8524
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Titre de la revue :
Advances in Data Analysis and Classification
Numéro :
9
Pagination :
20
Éditeur :
Springer Verlag
Date de publication :
2015-09
ISSN :
1862-5347
Mot(s)-clé(s) :
Non-Linear PCA
Data Analysis
Auto-Associative Models
Data Analysis
Auto-Associative Models
Discipline(s) HAL :
Statistiques [stat]/Applications [stat.AP]
Résumé en anglais : [en]
Auto-Associative models cover a large class of methods used in data analysis. In this paper, we describe the generals properties of these models when the projection component is linear and we propose and test an easy to ...
Lire la suite >Auto-Associative models cover a large class of methods used in data analysis. In this paper, we describe the generals properties of these models when the projection component is linear and we propose and test an easy to implement Probabilistic Semi-Linear Auto- Associative model in a Gaussian setting. We show it is a generalization of the PCA model to the semi-linear case. Numerical experiments on simulated datasets and a real astronomical application highlight the interest of this approachLire moins >
Lire la suite >Auto-Associative models cover a large class of methods used in data analysis. In this paper, we describe the generals properties of these models when the projection component is linear and we propose and test an easy to implement Probabilistic Semi-Linear Auto- Associative model in a Gaussian setting. We show it is a generalization of the PCA model to the semi-linear case. Numerical experiments on simulated datasets and a real astronomical application highlight the interest of this approachLire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CNRS
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:11:12Z
2020-06-09T07:19:52Z
2020-06-09T07:19:52Z
Fichiers
- documen
- Accès libre
- Accéder au document