The sparse matrix transform for covariance estimation and analysis of high dimensional signals

Covariance estimation for high dimensional signals is a classically difficult problem in statistical signal analysis and machine learning. In this paper, we propose a maximum likelihood (ML) approach to covariance estimation, which employs a novel non-linear sparsity constraint. More specifically, t...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 20(2011), 3 vom: 15. März, Seite 625-40
1. Verfasser:	Cao, Guangzhi (VerfasserIn)
Weitere Verfasser:	Bachega, Leonardo R, Bouman, Charles A
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2011
Zugriff auf das übergeordnete Werk:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:	Journal Article Research Support, U.S. Gov't, Non-P.H.S.