Example: FastICA on 2D point clouds

FastICA on 2D point clouds

This example illustrates visually in the feature space a comparison by results using two different component analysis techniques.

Independent component analysis (ICA) vs Principal component analysis (PCA).

Representing ICA in the feature space gives the view of ‘geometric ICA’: ICA is an algorithm that finds directions in the feature space corresponding to projections with high non-Gaussianity. These directions need not be orthogonal in the original feature space, but they are orthogonal in the whitened feature space, in which all directions correspond to the same variance.

PCA, on the ot