  Matrix factorisation methods decompose multivariate observations as linear
combinations of latent feature vectors. The Indian Buffet Process (IBP)
provides a way to model the number of latent features required for a good
approximation in terms of regularised reconstruction error. Previous work has
focussed on latent feature vectors with independent entries. We extend the
model to include nondiagonal latent covariance structures representing
characteristics such as smoothness. This is done by . Using simulations we
demonstrate that under appropriate conditions a smoothness prior helps to
recover the true latent features, while denoising more accurately. We
demonstrate our method on a real neuroimaging dataset, where computational
tractability is a sufficient challenge that the efficient strategy presented
here is essential.
