  Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of
extracting a linear structure from a high dimensional data based on estimating
a low-dimensional non-Gaussian data component. In this paper we discuss a new
approach to direct estimation of the projector on the target space based on
semidefinite programming which improves the method sensitivity to a broad
variety of deviations from normality. We also discuss the procedures which
allows to recover the structure when its effective dimension is unknown.
