The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 6, Number 1 (2012), 125-160.
A stochastic algorithm for probabilistic independent component analysis
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.
Ann. Appl. Stat., Volume 6, Number 1 (2012), 125-160.
First available in Project Euclid: 6 March 2012
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Allassonnière, Stéphanie; Younes, Laurent. A stochastic algorithm for probabilistic independent component analysis. Ann. Appl. Stat. 6 (2012), no. 1, 125--160. doi:10.1214/11-AOAS499. https://projecteuclid.org/euclid.aoas/1331043391
- Supplementary material: Supplement to “A stochastic algorithm for probabilistic independent component analysis”. This file presents a larger version of some of the images contained in this paper.