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Advances in Data Analysis and Classification

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01-09-2015 | Regular Article

Probabilistic auto-associative models and semi-linear PCA

Author: Serge Iovleff

Published in: Advances in Data Analysis and Classification | Issue 3/2015

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Abstract

Auto-associative models cover a large class of methods used in data analysis, including for example principal component analysis (PCA) and auto-associative neural networks. In this paper, we describe the general properties of these models when the projection component is linear and we propose and test an easy-to-implement probabilistic semi-linear auto-associative model in a Gaussian setting. We show that it is a generalization of the PCA model to the semi-linear case. Numerical experiments on simulated datasets and a real astronomical application highlight the interest of this approach.

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Title: Probabilistic auto-associative models and semi-linear PCA
Author: Serge Iovleff
Publication date: 01-09-2015
Publisher: Springer Berlin Heidelberg
Published in: Advances in Data Analysis and Classification / Issue 3/2015
Print ISSN: 1862-5347
Electronic ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-014-0185-3

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