Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56657
Authors: 
Diederichs, Elmar
Juditsky, Anatoli
Nemirovski, Arkadi
Spokoiny, Vladimir
Year of Publication: 
2011
Series/Report no.: 
SFB 649 discussion paper 2011-080
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.
Subjects: 
dimension reduction
non-Gaussian components analysis
feature extraction
JEL: 
C14
Document Type: 
Working Paper

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