Title :
A minor subspace analysis algorithm
Author :
Luo, Fa-Long ; Unbehauen, Rolf
Author_Institution :
Lehrstuhl fur Allgemeine und Theor. Elektrotech., Erlangen-Nurnberg Univ., Germany
fDate :
9/1/1997 12:00:00 AM
Abstract :
This paper proposes a learning algorithm which extracts adaptively the minor subspace spanned by the eigenvectors corresponding to the smallest eigenvalues of the autocorrelation matrix of an input signal. We show both analytically and by simulation results that the weight vectors provided by the proposed algorithm are guaranteed to converge to the minor subspace of the input signal. For wider applications, we also present the complex valued version of the proposed minor subspace analysis algorithm
Keywords :
adaptive systems; convergence of numerical methods; covariance matrices; eigenvalues and eigenfunctions; learning systems; neural nets; parameter estimation; signal processing; spectral analysis; adaptive systems; autocorrelation matrix; convergence; covariance matrix; eigenvalues; eigenvectors; learning algorithm; minor subspace analysis; neural networks; optimisation; parameter estimation; signal processing; spectral analysis; weight vectors; Adaptive signal processing; Additive noise; Algorithm design and analysis; Analytical models; Autocorrelation; Convergence; Covariance matrix; Eigenvalues and eigenfunctions; Signal analysis; Signal processing algorithms;
Journal_Title :
Neural Networks, IEEE Transactions on
