Auto-Associative Models and Generalized ...
Type de document :
Article dans une revue scientifique: Article original
Titre :
Auto-Associative Models and Generalized Principal Component Analysis
Auteur(s) :
Girard, Stéphane [Auteur]
Modelling and Inference of Complex and Structured Stochastic Systems [?-2006] [MISTIS [?-2006]]
Iovleff, Serge [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Modelling and Inference of Complex and Structured Stochastic Systems [?-2006] [MISTIS [?-2006]]
Iovleff, Serge [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Titre de la revue :
Journal of Multivariate Analysis
Pagination :
21-39
Éditeur :
Elsevier
Date de publication :
2005
ISSN :
0047-259X
Discipline(s) HAL :
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Statistiques [stat]/Théorie [stat.TH]
Résumé en anglais : [en]
In this paper, we propose auto-associative (AA) models to generalize Principal component analysis (PCA). AA models have been introduced in data analysis from a geometrical point of view. They are based on the approximation ...
Lire la suite >In this paper, we propose auto-associative (AA) models to generalize Principal component analysis (PCA). AA models have been introduced in data analysis from a geometrical point of view. They are based on the approximation of the observations scatter-plot by a differentiable manifold. In this paper, they are interpreted as Projection pursuit models adapted to the auto-associative case. Their theoretical properties are established and are shown to extend the PCA ones. An iterative algorithm of construction is proposed and its principle is illustrated both on simulated and real data from image analysis.Lire moins >
Lire la suite >In this paper, we propose auto-associative (AA) models to generalize Principal component analysis (PCA). AA models have been introduced in data analysis from a geometrical point of view. They are based on the approximation of the observations scatter-plot by a differentiable manifold. In this paper, they are interpreted as Projection pursuit models adapted to the auto-associative case. Their theoretical properties are established and are shown to extend the PCA ones. An iterative algorithm of construction is proposed and its principle is illustrated both on simulated and real data from image analysis.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
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