The topic of my PhD was Independent Component Analysis (ICA).
Picard stands for “Preconditioned ICA for Real Data”. This algorithm quickly solves maximum-likelihood ICA. It is detailed in Faster ICA by preconditioning with Hessian approximations.
Picard-O is an adaptation of Picard which solves the same problem as FastICA, while being much faster on real data. It can also separate super- and sub- Gaussian sources. It is detailed in Faster ICA under orthogonal constraint.
Python and matlab code is available online at https://pierreablin.github.io/picard/.