Probabilistic Auto-Associative Models and Semi-Linear PCA
Applications
2012-09-21 v1 Machine Learning
Abstract
Auto-Associative models cover a large class of methods used in data analysis. In this paper, we describe the generals 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 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
Cite
@article{arxiv.1209.4551,
title = {Probabilistic Auto-Associative Models and Semi-Linear PCA},
author = {Serge Iovleff},
journal= {arXiv preprint arXiv:1209.4551},
year = {2012}
}