Sparse non Gaussian component analysis by semidefinite programming

Elmar Diederichs, Anatoli Juditsky, Arkadi Nemirovski and Vladimir Spokoiny

No 2011-080, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: 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: dimension reduction; non-Gaussian components analysis; feature extraction (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2011
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