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
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;
Conference_Titel :
Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop
Conference_Location :
North Falmouth, MA
Print_ISBN :
0-7803-7196-8
DOI :
10.1109/NNSP.2001.943131
