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Title:Estimation of the signal subspace without estimation of the inverse covariance matrix PDF Logo
Authors:Panov, Vladimir
Issue Date:2010
Series/Report no.:SFB 649 discussion paper 2010-050
Abstract:Let a high-dimensional random vector X can be represented as a sum of two components - a signal S , which belongs to some low-dimensional subspace S, and a noise component N . This paper presents a new approach for estimating the subspace S based on the ideas of the Non-Gaussian Component Analysis. Our approach avoids the technical difficulties that usually exist in similar methods - it doesn't require neither the estimation of the inverse covariance matrix of X nor the estimation of the covariance matrix of N.
Subjects:dimension reduction
non-Gaussian components
NGCA
JEL:C13
C14
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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