Robust least squares methods under bounded data uncertainties
Author
Vanli, N. D.
Donmez, M. A.
Kozat, S. S.
Date
2015Source Title
Digital Signal Processing
Print ISSN
1051-2004
Publisher
Academic Press
Volume
36
Pages
82 - 92
Language
English
Type
ArticleItem Usage Stats
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Abstract
We study the problem of estimating an unknown deterministic signal that is observed through an unknown deterministic data matrix under additive noise. In particular, we present a minimax optimization framework to the least squares problems, where the estimator has imperfect data matrix and output vector information. We define the performance of an estimator relative to the performance of the optimal least squares (LS) estimator tuned to the underlying unknown data matrix and output vector, which is defined as the regret of the estimator. We then introduce an efficient robust LS estimation approach that minimizes this regret for the worst possible data matrix and output vector, where we refrain from any structural assumptions on the data. We demonstrate that minimizing this worst-case regret can be cast as a semi-definite programming (SDP) problem. We then consider the regularized and structured LS problems and present novel robust estimation methods by demonstrating that these problems can also be cast as SDP problems. We illustrate the merits of the proposed algorithms with respect to the well-known alternatives in the literature through our simulations.
Keywords
Data estimationLeast squares
Additive noise
Estimation
Matrix algebra
Optimization
Minimax
Regret
Robust
Least squares approximations