Estimation of the signal subspace without estimation of the inverse covariance matrix
- Panov, Vladimir A.
2010 Mathematics Subject Classification
- 62G05 62H99 60G35
- dimension reduction, non-Gaussian components, NGCA
Let a high-dimensional random vector $vecX$ can be represented as a sum of two components - a signal $vecS$, 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 $vecN$.