PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Auto-Associative Models and Generalized Principal Component Analysis
Stephane Girard and Serge IOVLEFF
Journal of Multivariate Analysis Volume 93, Number 1, pp. 21-39, 2005.

Abstract

In this paper, 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. In this paper, 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.

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Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:2314
Deposited By:Stephane Girard
Deposited On:17 November 2006