Title :
A deterministic analysis of variable-metric adaptive filtering algorithms under small metric-fluctuations
Author :
Yukawa, Masahiro ; Yamada, Isao
Author_Institution :
BSI Math. Neurosci. Lab., RIKEN, Wako, Japan
Abstract :
We present a rigorous deterministic analysis of the variable-metric adaptive filtering algorithms (including the transform-domain, LMS/Newton, and proportionate adaptive filters) by using the framework of variable-metric adaptive projected subgradient method (Yukawa et al. 2007). Under small metric-fluctuations, we present the useful properties of (i) monotone approximation - with respect to a certain constant metric - indicating the stability of the algorithm and (ii) convergence to an asymptotically optimal point. Numerical examples show the advantage of the variable-metric adaptive filtering algorithms and suggest the validity of the analysis.
Keywords :
adaptive filters; convergence of numerical methods; filtering theory; gradient methods; adaptive projected subgradient method; asymptotically optimal point; convergence; monotone approximation; small metric-fluctuation; variable-metric adaptive filtering algorithm; Adaptive filters; Algorithm design and analysis; Approximation algorithms; Asymptotic stability; Convergence; Filtering algorithms; Least squares approximation; Linear systems; Neuroscience; Vectors; Adaptive filtering; deterministic convergence analysis; metric-projection; proportionate adaptive filter;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5495869
