It uses 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 mutually statistically independent 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 is important to blind signal separation, EEG analysis and FMRI analysis.

This concept can be extended to analyse non physical signals, for instance ICA has been aplied to discover discussions topics on a bag of news list archives.

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