Auto-associative models and generalized principal component analysis

Stéphane Girard and Serge Iovleff

Journal of Multivariate Analysis, 2005, vol. 93, issue 1, 21-39

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.

Keywords: Auto-associative; models; Principal; component; analysis; Projection; pursuit; Regression (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (2)

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