PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Inlier-Based ICA with an Application to Super-imposed Images
Frank Meinecke, Stefan Harmeling and Klaus-Robert Müller
International Journal of Imaging Systems and Technology Volume 15, Number 1, pp. 48-55, 2005.

Abstract

This paper proposes a new independent component analysis (ICA) method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images. Furthermore, the method is designed to be robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers. Our approach is based on a simple outlier index. However, instead of robustifying an existing algorithm by some outlier rejection technique we show how this index can be used directly to solve the ICA problem for super-Gaussian sources. The resulting inlierbased ICA (IBICA) is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed signals).

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1888
Deposited By:Klaus-Robert Müller
Deposited On:29 December 2005