Soft dimension reduction for ICA by joint diagonalization on the stiefel manifold

Abstract

Joint diagonalization for ICA is often performed on the orthogonal group after a pre-whitening step. Here we assume that we only want to extract a few sources after pre-whitening, and hence work on the Stiefel manifold of p-frames in Rn. The resulting method does not only use second-order statistics to estimate the dimension reduction and is therefore denoted as soft dimension reduction. We employ a trust- region method for minimizing the cost function on the Stiefel manifold. Applications to a toy example and functional MRI data show a higher numerical efficiency, especially when p is much smaller than n,and more robust performance in the presence of strong noise than methods based on pre-whitening. © Springer-Verlag Berlin Heidelberg 2009.