Auto-Associative Models and Generalized Principal Component Analysis - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 2002

Auto-Associative Models and Generalized Principal Component Analysis

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.
Fichier principal
Vignette du fichier
RR-4364.pdf (817.2 Ko) Télécharger le fichier

Dates and versions

inria-00072224 , version 1 (23-05-2006)

Identifiers

  • HAL Id : inria-00072224 , version 1

Cite

Stéphane Girard, Serge Iovleff. Auto-Associative Models and Generalized Principal Component Analysis. [Research Report] RR-4364, INRIA. 2002. ⟨inria-00072224⟩
150 View
202 Download

Share

Gmail Facebook Twitter LinkedIn More