(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2010) Aizenman, Michael; Jansen, Sabine; Jung, Paul
Let a high-dimensional random vector vecX can be represented as a sum of two components - a signa vecS, which belongs to some low-dimensional subspace mathcalS, and a noise component vecN. This paper presents a new approach for estimating the subspace mathcalS 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 vecX nor the estimation of the covariance matrix of vecN
