A Fast Adaptive Algorithm for the Generalized Symmetric Eigenvalue Problem

Author

Attallah, Samir ; Abed-Meraim, Karim

Author_Institution

Sch. of Sci. & Technol., SIM Univ., Singapore

Volume

15

fYear

2008

fDate

6/30/1905 12:00:00 AM

Firstpage

797

Lastpage

800

Abstract

In this letter, we propose a new adaptive algorithm for the generalized symmetric eigenvalue problem, which can extract the principal and minor generalized eigenvectors, as well as their corresponding subspaces, at a low computational cost. A comparison with other adaptive algorithms from the literature, including the batch generalized singular value decomposition (GSVD) technique, is also given to show the superiority of the proposed algorithm in terms of convergence performance and computational complexity.

Keywords

adaptive signal processing; eigenvalues and eigenfunctions; singular value decomposition; adaptive algorithm; batch generalized singular value decomposition; computational complexity; convergence performance; generalized eigenvectors; generalized symmetric eigenvalue problem; Adaptive algorithm; Computational complexity; Computational efficiency; Convergence; Covariance matrix; Eigenvalues and eigenfunctions; Matrix decomposition; Principal component analysis; Resonance light scattering; Signal processing algorithms; Adaptive algorithm; fast estimation and tracking; generalized eigenvalue problem; generalized eigenvectors; generalized subspace estimation;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2008.2006346

Filename

4682558