Sparse non Gaussian component analysis by semidefinite programming - Elmar Diederichs; Anatoli Juditsky; Arkadi Nemirovski; Vladimir Spokoiny (Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, 2011)
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.
Keywords: C14; 330; dimension reduction; non-Gaussian components analysis; feature extraction