Mean-square performance of the hyperslab-based adaptive projected subgradient method

Author

Wee, Wemer M. ; Yamagishi, M. ; Yamada, Isao

Author_Institution

Dept. of Commun. & Comput. Eng., Tokyo Inst. of Technol., Tokyo, Japan

fYear

2014

fDate

4-9 May 2014

Firstpage

6384

Lastpage

6388

Abstract

This paper is concerned with the mean-square performance of the hyperslab-based adaptive projected subgradient method, a set theoretic estimation tool that has been successfully applied in a wide variety of signal processing tasks. Using energy-conservation arguments, general performance results are derived without restricting the regression data to being Gaussian or white. Numerical simulations are provided to illustrate the theoretical developments.

Keywords

adaptive estimation; gradient methods; numerical analysis; signal processing; energy-conservation argument; hyperslab-based adaptive projected subgradient method; mean-square performance; numerical simulation; regression data restriction; set theoretic estimation tool; signal processing; Algorithm design and analysis; Projection algorithms; Robustness; Signal processing algorithms; Stability analysis; Steady-state; Vectors; Adaptive filters; data-reusing algorithms; energy conservation; error nonlinearity; mean-square performance;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on

Conference_Location

Florence

Type

conf

DOI

10.1109/ICASSP.2014.6854833

Filename

6854833