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.