To apply score function difference based ICA algorithms to high-dimensional data

Recently, the score function difference (SFD) has been applied to develop ICA algorithms. But such algorithms are not suitable for highdimensional data because the SFD estimation in a high-dimensional space is problematic. In this paper, by investigating the relationship between mutual independence and pairwise independence, we develop an approach for ICA with linear instantaneous mixtures and convolutive mixtures based on pairwise independence. This approach only involves the computation of the 2-dimensional SFD and can be directly applied to high-dimensional data. The experimental result illustrates the usefulness of this approach.