Faster ICA under orthogonal constraint

Abstract : Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences. In its classical form, ICA relies on modeling the data as a linear mixture of non-Gaussian independent sources. The problem can be seen as a likelihood maximization problem. We introduce Picard-O, a preconditioned L-BFGS strategy over the set of orthogonal matrices, which can quickly separate both super-and sub-Gaussian signals. It returns the same set of sources as the widely used FastICA algorithm. Through numerical experiments, we show that our method is faster and more robust than FastICA on real data.
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Pré-publication, Document de travail
2017
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Soumis le : mercredi 29 novembre 2017 - 15:33:10
Dernière modification le : mercredi 19 septembre 2018 - 01:27:23

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  • HAL Id : hal-01651842, version 1

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Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort. Faster ICA under orthogonal constraint. 2017. 〈hal-01651842〉

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