Skip to Main content Skip to Navigation
Reports

Auto-Associative Models and Generalized Principal Component Ana= lysis

Stéphane Girard 1 Serge Iovleff 2
1 IS2 - Statistical Inference for Industry and Health
Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
Abstract : In this paper, we propose the auto-associative (AA) model to generalize the 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 scatterplot by a differentiable manifold. We propose here to interpret them as Projection Pursuit models adapted to the auto-associative case. We establish their theoretical properties and show how they 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.
Document type :
Reports
Complete list of metadata

https://hal.inria.fr/inria-00072224
Contributor : Rapport de Recherche Inria <>
Submitted on : Tuesday, May 23, 2006 - 8:09:17 PM
Last modification on : Tuesday, January 19, 2021 - 11:08:37 AM
Long-term archiving on: : Sunday, April 4, 2010 - 10:59:18 PM

Identifiers

  • HAL Id : inria-00072224, version 1

Citation

Stéphane Girard, Serge Iovleff. Auto-Associative Models and Generalized Principal Component Ana= lysis. [Research Report] RR-4364, INRIA. 2002. ⟨inria-00072224⟩

Share

Metrics

Record views

400

Files downloads

472