DocumentCode
3373430
Title
An RLS type algorithm for generalized eigendecomposition
Author
Rao, Yadunandana N. ; Principe, Jose C.
Author_Institution
Computational NeuroEngineering Lab., Florida Univ., Gainesville, FL, USA
fYear
2001
fDate
2001
Firstpage
263
Lastpage
272
Abstract
Eigendecompositions play a very important role in a variety of signal processing applications. The authors derive and study an algorithm for Generalized Eigendecomposition which is both online and fast converging. A rule to extract the maximum eigencomponent is first presented, and then online deflation is applied to estimate the minor components. Proof of convergence has been established using stochastic approximation theory
Keywords
approximation theory; convergence; eigenvalues and eigenfunctions; signal processing; RLS type algorithm; generalized eigendecomposition; maximum eigencomponent; minor components; online deflation; signal processing applications; stochastic approximation theory; Convergence; Covariance matrix; Eigenvalues and eigenfunctions; Equations; Filtering algorithms; Linear discriminant analysis; Principal component analysis; Resonance light scattering; Signal processing algorithms; Symmetric matrices;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop
Conference_Location
North Falmouth, MA
ISSN
1089-3555
Print_ISBN
0-7803-7196-8
Type
conf
DOI
10.1109/NNSP.2001.943131
Filename
943131
Link To Document