Title :
The exponential stability of the invariant-norm PCA algorithm
Author :
Reif, Konrad ; Luo, Fa-Long ; Unbehauen, Rolf
Author_Institution :
Lehrstuhl fur Allgemeine und Theor. Elektrotech., Erlangen-Nurnberg Univ., Germany
fDate :
10/1/1997 12:00:00 AM
Abstract :
This brief investigates a recently proposed principal component analysis (PCA) algorithm. We prove that that the solutions of the corresponding differential equations converge to the principal eigenvectors of the autocorrelation matrix and calculate an exponential decaying bound for the error
Keywords :
adaptive signal processing; asymptotic stability; correlation methods; differential equations; eigenvalues and eigenfunctions; matrix algebra; neural nets; adaptive signal processing; autocorrelation matrix; differential equations; error; exponential decaying bound; exponential stability; invariant-norm PCA algorithm; linear single-layer network; neural networks; principal component analysis; principal eigenvectors; stochastic approximation; Autocorrelation; Circuits; Differential equations; Digital filters; Digital signal processing; Principal component analysis; Signal processing; Signal processing algorithms; Stability; System identification;
Journal_Title :
Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on
