Probabilistic auto-associative models and semi-linear PCA

Serge Iovleff ()

Advances in Data Analysis and Classification, 2015, vol. 9, issue 3, 267-286

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. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Auto-associative models; Non-linear PCA; Dimension reduction; Probabilistic non-linear PCA; 62-07; 62H25 (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s11634-014-0185-3

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