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Independent Component Analysis (ICA) is a technique for unsupervisedexploration of multi-channel data widely used in observational sciences. In itsclassical form, ICA relies on modeling the data as a linear mixture ofnon-Gaussian independent sources. The problem can be seen as a likelihoodmaximization problem. We introduce Picard-O, a preconditioned L-BFGS strategyover the set of orthogonal matrices, which can quickly separate both super- andsub-Gaussian signals. It returns the same set of sources as the widely usedFastICA algorithm. Through numerical experiments, we show that our method isfaster and more robust than FastICA on real data.

Faster ICA under orthogonal constraint