PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robust ICA for Super-Gaussian Sources
Frank Meinecke, Stefan Harmeling and Klaus-Robert Müller
In: 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2004), 22-24 Sep 2004, Granada, Spain.

Abstract

Most ICA algorithms are sensitive to outliers. Instead of robustifying existing algorithms by outlier rejection techniques, we show how a simple outlier index can be used directly to solve the ICA problem for super-Gaussian source signals. This ICA method is outlier-robust by construction and can be used for standard ICA as well as for over-complete ICA (i.e.~more source signals than observed signals (mixtures)).

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1901
Deposited By:Stefan Harmeling
Deposited On:29 December 2005