Sparse Representation Using Stepwise Tikhonov Regularization With Offline Computations

被引：1

作者：

Delgado, Ramon A. ^{[1
]}

Middleton, Richard H. ^{[1
]}

机构：

[1] Univ Newcastle, Sch Elect Engn & Comp, Callaghan, NSW 2308, Australia

来源：

IEEE SIGNAL PROCESSING LETTERS | 2019年 / 26卷 / 06期

关键词：

Greedy algorithms; least squares approximation; matching pursuit algorithms; ORTHOGONAL LEAST-SQUARES; CONSTRAINTS; RECOVERY;

D O I：

10.1109/LSP.2019.2907370

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This letter describes a novel algorithm for sparse reconstruction. The method uses offline computations to reduce the computational burden of online execution. The approach relies on the recently proposed stepwise Tikhonov regularization (STIR) method to implement forward selection procedures such as orthogonal least squares (OLS), orthogonal matching pursuit (OMP), and STIR. Numerical simulations show the efficacy of the proposed approach, which is competitive against state-of-the-art implementation of OLS and OMP.

引用

页码：873 / 877

页数：5

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