Title :

Auto-associative models, nonlinear Principal component analysis, manifolds and projection pursuit

Author(s) :

Girard, Stéphane [Auteur]
Modelling and Inference of Complex and Structured Stochastic Systems [MISTIS]
Iovleff, Serge [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Scientific editor(s) :

Alexander N. Gorban
Balázs Kégl
Donald C. Wunsch
and Andrei Y. Zinovyev

Book title :

Principal Manifolds for Data Visualisation and Dimension Reduction

Publisher :

Springer-Verlag

Publication date :

2008

ISBN :

978-3-540-73749-0

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]

English abstract : [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. ...
Show more >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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Comment :

volume 58

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL