A stochastic algorithm for probabilistic independent component analysis

Details A stochastic algorithm for probabilistic independent component analysis A stochastic algorithm for probabilistic independent component analysis

2012-03-16

arxiv.org/abs/1203.3712v1

Annals of Applied Statistics 2012, Vol. 6, No. 1, 125-160

Statistics

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Published in at http://dx.doi.org/10.1214/11-AOAS499 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

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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