Open Access
March 2012 A stochastic algorithm for probabilistic independent component analysis
Stéphanie Allassonnière, Laurent Younes
Ann. Appl. Stat. 6(1): 125-160 (March 2012). DOI: 10.1214/11-AOAS499


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.


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Stéphanie Allassonnière. Laurent Younes. "A stochastic algorithm for probabilistic independent component analysis." Ann. Appl. Stat. 6 (1) 125 - 160, March 2012.


Published: March 2012
First available in Project Euclid: 6 March 2012

zbMATH: 1358.62060
MathSciNet: MR2951532
Digital Object Identifier: 10.1214/11-AOAS499

Keywords: EM algorithm , image analysis , Independent component analysis , independent factor analysis , LaTeXe 2ε , statistical modeling , stochastic approximation

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 1 • March 2012
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