Faster independent component analysis by preconditioning with
Hessian approximations

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.

Joint work with P Ablin and J-F Cardoso