Auto-Associative models and generalized ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès sans actes
URL permanente :
Titre :
Auto-Associative models and generalized Principal Component Analysis
Auteur(s) :
Titre de la manifestation scientifique :
Workshop on principal manifolds for data cartography and dimension reduction
Ville :
Leicester
Pays :
Royaume-Uni
Date de début de la manifestation scientifique :
2006-08
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
In this communication, 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 ...
Lire la suite >In this communication, 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. 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 communication, 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. 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
Audience :
Internationale
Vulgarisation :
Oui
Établissement(s) :
CNRS
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:10:42Z
2020-06-09T08:28:52Z
2020-06-09T08:28:52Z
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