Sparse non Gaussian component analysis by semidefinite programming

Elmar Diederichs; Anatoli Juditsky; Arkadi Nemirovski; Vladimir Spokoiny

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. -- dimension reduction ; non-Gaussian components analysis ; feature extraction

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Year of publication:	2011
Authors:	Diederichs, Elmar ; Juditsky, Anatoli ; Nemirovskij, Arkadij S. ; Spokojnyj, Vladimir G.
Publisher:	Berlin : SFB 649, Economic Risk
Subject:	Hauptkomponentenanalyse \| Principal component analysis \| Schätztheorie \| Estimation theory \| Mathematische Optimierung \| Mathematical programming \| Theorie \| Theory