Abstract
In this work, we propose a new regularization approach for linear least-squares problems with random matrices. In the proposed constrained perturbation regularization approach, an artificial perturbation matrix with a bounded norm is forced into the system model matrix. This perturbation is introduced to improve the singular-value structure of the model matrix and, hence, the solution of the estimation problem. Relying on the randomness of the model matrix, a number of deterministic equivalents from random matrix theory are applied to derive the near-optimum regularizer that minimizes the mean-squared error of the estimator. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods for various estimated signal characteristics. In addition, simulations show that our approach is robust in the presence of model uncertainty.
Original language | English (US) |
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Pages (from-to) | 1727-1731 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 23 |
Issue number | 12 |
DOIs | |
State | Published - Oct 6 2016 |
Bibliographical note
KAUST Repository Item: Exported on 2023-08-04Acknowledgements: This work was supported by a CRG3 Grant ORS#221 from the Office of Competitive Research, King Abdullah University of Science and Technology.
ASJC Scopus subject areas
- Signal Processing
- Applied Mathematics
- Electrical and Electronic Engineering