Sparse Non Gaussian Component Analysis by Semidefinite Programming
  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. 
Elmar Diederichs, Anatoli Juditsky, Arkadi Nemirovski, and Vladimir   Spokoiny
math.ST math.OC stat.CO stat.ML stat.TH