Inlier-based ICA with an application to superimposed images
This paper proposes a new ICA method which is able to unmix overcomplete mixtures of 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 does not robustify an existing algorithm by some outlier detection technique. Instead we show that a simple outlier index can be used directly to solve the ICA problem for super-Gaussian source signals. Our inlier-based ICA (abbr. 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).