Sequential Fixed-Point ICA Based on Mutual Information Minimization

A new gradient technique is introduced for linear ICA based on the Edgeworth expansion of mutual information, for which the algorithm operates sequentially using fixed-point iterations. In order to address the adverse effect of outliers, a robust version of the Edgeworth expansion is adopted, in terms of robust cumulants, and robust derivatives of the Hermite polynomials are used. Also, a new constrained version of ICA is introduced, based on goal programming of mutual information objectives, and which is applied to the extraction of the antepartum fetal electrocardiogram (ECG) from multi-electrode cutaneous recordings on the mother's thorax and abdomen. 1 

• Type: Journal
• Year: 2008
• Where: NECO
• Authors: Marc M. Van Hulle
• Keywords: Edgeworth Expansion, Mutual Information, NECO 2008, Robust