DocumentCode
1895318
Title
A minimax approach for mean square denoising
Author
Pesquet, Jean-Christophe ; Eldar, Yonina
Author_Institution
Institut Gaspard Monge, Univ. de Marne la Vallee
fYear
2005
fDate
17-20 July 2005
Firstpage
785
Lastpage
790
Abstract
Minimax estimation aims at finding optimal estimators in the worst case situation compatible with the available information. In the present work, we consider the minimax mean square denoising of a random vector using a nonlinear estimator. The data set over which the minimax estimator is looked for takes the general form of a convex set where the correlation matrix of the data is constrained to lie. Also, additional convex constraints on the weights defining the estimator can be taken into account in the proposed approach
Keywords
correlation methods; matrix algebra; mean square error methods; minimax techniques; nonlinear estimation; random processes; signal denoising; correlation matrix; mean square denoising; minimax approach; minimax estimation; nonlinear estimator; random vector; Additive noise; Constraint optimization; Estimation; Focusing; Gaussian noise; Linear matrix inequalities; Minimax techniques; Noise reduction; Signal processing; Statistics;
fLanguage
English
Publisher
ieee
Conference_Titel
Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on
Conference_Location
Novosibirsk
Print_ISBN
0-7803-9403-8
Type
conf
DOI
10.1109/SSP.2005.1628700
Filename
1628700
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