Probabilistic Auto-Associative Models and Semi-Linear PCA

Iovleff, Serge

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1007/s11634-014-0185-3

Title :

Probabilistic Auto-Associative Models and Semi-Linear PCA

Author(s) :

Iovleff, Serge [Auteur]

Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]

Journal title :

Advances in Data Analysis and Classification

Pages :

Publisher :

Springer Verlag

Publication date :

2015-09

ISSN :

1862-5347

English keyword(s) :

Auto-Associative Models
Data Analysis
Non-Linear PCA

HAL domain(s) :

Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

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 ...
Show more >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 approachShow less >

Language :

Anglais

Popular science :

Non

Collections :