Title :
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
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;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
DOI :
10.1109/ICASSP.2014.6854833
