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arXiv · 1203.3712

A stochastic algorithm for probabilistic independent component analysis

Stéphanie Allassonniére·Laurent Younes
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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.

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Stéphanie Allassonniére, Laurent Younes. 2012-03-16. A stochastic algorithm for probabilistic independent component analysis. https://doi.org/10.1214/11-aoas499

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