  Non-gaussian component analysis (NGCA) introduced in offered a method for
high dimensional data analysis allowing for identifying a low-dimensional
non-Gaussian component of the whole distribution in an iterative and structure
adaptive way. An important step of the NGCA procedure is identification of the
non-Gaussian subspace using Principle Component Analysis (PCA) method. This
article proposes a new approach to NGCA called sparse NGCA which replaces the
PCA-based procedure with a new the algorithm we refer to as convex projection.
