2017/06/25 by Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort · 1 voice · 1 citation
Mathematics · #stat.AP #stat.ML
paper · pdf · doi:10.1109/tsp.2018.2844203
23 pages, 3 figures
arxiv published 2017/06/25 · arxiv created 2017/09/08 · arxiv updated 2018/08/01
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.