  Independent component analysis (ICA) is a powerful method for blind source
separation based on the assumption that sources are statistically independent.
Though ICA has proven useful and has been employed in many applications,
complete statistical independence can be too restrictive an assumption in
practice. Additionally, important prior information about the data, such as
sparsity, is usually available. Sparsity is a natural property of the data, a
form of diversity, which, if incorporated into the ICA model, can relax the
independence assumption, resulting in an improvement in the overall separation
performance. In this work, we propose a new variant of ICA by entropy bound
minimization (ICA-EBM)-a flexible, yet parameter-free algorithm-through the
direct exploitation of sparsity. Using this new SparseICA-EBM algorithm, we
study the synergy of independence and sparsity through simulations on synthetic
as well as functional magnetic resonance imaging (fMRI)-like data.
