2010-10-14Buch DOI: 10.18452/4278
Estimation of the signal subspace without estimation of the inverse covariance matrix
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.
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Is Part Of Series: Sonderforschungsbereich 649: Ökonomisches Risiko - 50, SFB 649 Papers, ISSN:1860-5664
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