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August 13, 2026
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Estimation of the signal subspace without estimation of the inverse covariance matrix

2010. No. 2010-050.
Panov V.
Let a high-dimensional random vector $\vX$ be represented as a sum of two components - a  signal $\vS$ that belongs to some low-dimensional linear subspace $\S$,  and a noise component $\vN$.  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 appear in similar methods - it requires neither the estimation of the inverse covariance  matrix of $\vX$ nor the estimation of the covariance matrix of $\vN$.
Priority areas: mathematics
Language: English
Text on another site
Keywords: снижение размерностиdimension reductionnon-Gaussian componentssignal subspaceнегауссовские компонентыпространство сигнала
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