A stochastic algorithm for probabilistic independent component analysis
Abstract
The decomposition of a sample of images on a relevant subspace is a recurrent problem in many different fields from Computer Vision to medical image analysis. We propose in this paper a new learning principle and implementation of the generative decomposition model generally known as noisy ICA (for independent component analysis) based on the SAEM algorithm, which is a versatile stochastic approximation of the standard EM algorithm. We demonstrate the applicability of the method on a large range of decomposition models and illustrate the developments with experimental results on various data sets.
Cite
@article{arxiv.1203.3712,
title = {A stochastic algorithm for probabilistic independent component analysis},
author = {Stéphanie Allassonniére and Laurent Younes},
journal= {arXiv preprint arXiv:1203.3712},
year = {2012}
}
Comments
Published in at http://dx.doi.org/10.1214/11-AOAS499 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)