Use the principal components analysis as a first step. Apply whitening[?] of the data, and then apply an iterative algorithm (such as an adaptive filter).
The ICA searches for orthogonal components that are statistically independent between them to describe the input data matrix (composed by the features vectors). This can allow the reconstruction of source signals from several corrupted mixtures thereof.
The method was invented by Erkky Oja[?], and is important to blind signal separation[?], EEG analysis and FMRI analysis.
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