Faster independent component analysis by preconditioning with Hessian approximations

Abstract : Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences. In its classic form, ICA relies on modeling the data as linear mixtures of non-Gaussian independent sources. The maximization of the corresponding likelihood is a challenging problem if it has to be completed quickly and accurately on large sets of real data. We introduce the Preconditioned ICA for Real Data (Picard) algorithm, which is a relative L-BFGS algorithm preconditioned with sparse Hessian approximations. Extensive numerical comparisons to several algorithms of the same class demonstrate the superior performance of the proposed technique, especially on real data, for which the ICA model does not necessarily hold.
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Pré-publication, Document de travail
2017
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Contributeur : Pierre Ablin <>
Soumis le : vendredi 8 septembre 2017 - 18:50:18
Dernière modification le : jeudi 5 octobre 2017 - 13:57:22

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  • HAL Id : hal-01552340, version 2
  • ARXIV : 1706.08171

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Pierre Ablin, Jean-Francois Cardoso, Alexandre Gramfort. Faster independent component analysis by preconditioning with Hessian approximations. 2017. 〈hal-01552340v2〉

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