Auto-associative models, nonlinear Principal ...
Type de document :
Partie d'ouvrage
Titre :
Auto-associative models, nonlinear Principal component analysis, manifolds and projection pursuit
Auteur(s) :
Girard, Stéphane [Auteur]
Modelling and Inference of Complex and Structured Stochastic Systems [MISTIS]
Iovleff, Serge [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Modelling and Inference of Complex and Structured Stochastic Systems [MISTIS]
Iovleff, Serge [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Éditeur(s) ou directeur(s) scientifique(s) :
Alexander N. Gorban
Balázs Kégl
Donald C. Wunsch
and Andrei Y. Zinovyev
Balázs Kégl
Donald C. Wunsch
and Andrei Y. Zinovyev
Titre de l’ouvrage :
Principal Manifolds for Data Visualisation and Dimension Reduction
Éditeur :
Springer-Verlag
Date de publication :
2008
ISBN :
978-3-540-73749-0
Discipline(s) HAL :
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Statistiques [stat]/Théorie [stat.TH]
Résumé en anglais : [en]
Auto-associative models have been introduced as a new tool for building nonlinear Principal component analysis (PCA) methods. Such models rely on successive approximations of a dataset by manifolds of increasing dimensions. ...
Lire la suite >Auto-associative models have been introduced as a new tool for building nonlinear Principal component analysis (PCA) methods. Such models rely on successive approximations of a dataset by manifolds of increasing dimensions. In this chapter, we propose a precise theoretical comparison between PCA and autoassociative models. We also highlight the links between auto-associative models, projection pursuit algorithms, and some neural network approaches. Numerical results are presented on simulated and real datasets.Lire moins >
Lire la suite >Auto-associative models have been introduced as a new tool for building nonlinear Principal component analysis (PCA) methods. Such models rely on successive approximations of a dataset by manifolds of increasing dimensions. In this chapter, we propose a precise theoretical comparison between PCA and autoassociative models. We also highlight the links between auto-associative models, projection pursuit algorithms, and some neural network approaches. Numerical results are presented on simulated and real datasets.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Commentaire :
volume 58
Collections :
Source :
Fichiers
- 1103.6119
- Accès libre
- Accéder au document