Semiparametric estimation of the signal subspace

Journal of machine learning and data analysis. 2012. Vol. 1. No. 3. P. 140–147.

Belomestny D., Панов В. А., Spokoiny 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.

Ключевые слова: dimension reduction, non-Gaussian components, signal subspace