Sparse Gaussian noisy independent component analysis

There are two main approaches to independent component analysis (ICA); maximization of non-Gaussianity of the sources and the exploitation of temporal correlation in Gaussian sources. In this paper, we present a novel sparse noisy ICA model where we have introduced temporal correlation in the sources, described by a first order auto regressive (AR(1)) process. The correlation structure of the sources eliminates the rotational invariance of the estimates, enabling their separation. Using simulated data, we demonstrate both source separation and denoising, where we compare our results to a sparse PCA method and the fastICA method. Additionally, we apply the method on a real hyperspectral dataset.