A stochastic algorithm for probabilistic independent component analysis

TL;DR

This paper introduces a stochastic algorithm based on the SAEM method for probabilistic independent component analysis, enabling effective decomposition of image data across various applications.

Contribution

It presents a new stochastic learning approach for noisy ICA using the SAEM algorithm, enhancing the flexibility and applicability of probabilistic decomposition models.

Findings

Effective decomposition demonstrated on diverse datasets

Versatile application across different image analysis tasks

Improved convergence properties of the stochastic algorithm

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.