The exponential stability of the invariant-norm PCA algorithm

IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing

Volumn 44, Issue 10, 1997, Pages 873-875

  Reif, Konrad ^a   Luo, Fa Long ^a   Unbehauen, Rolf ^a  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

Author keywords

Adaptive signal processing; Eigenvectors; Neural networks; Unsupervised learning

Indexed keywords

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; CORRELATION METHODS; DIFFERENTIAL EQUATIONS; EIGENVALUES AND EIGENFUNCTIONS; LEARNING SYSTEMS; MATRIX ALGEBRA; NEURAL NETWORKS; VECTORS;

ADAPTIVE SIGNAL PROCESSING; PRINCIPAL COMPONENT ANALYSIS (PCA) ALGORITHMS;

SIGNAL PROCESSING;

EID: 0031259372     PISSN: 10577130     EISSN: None     Source Type: Journal    
DOI: 10.1109/82.633449     Document Type: Article

References (7)

1. J. Karhunen and J. Joutsensalo, "Representation and separation of signals using nonlinear PCA type learning," Neural Networks, vol. 7, 1994, pp. 113-128.
2. F.-L. Luo and R. Unbehauen, Applied Neural Networks for Signal Processing. Cambridge, U.K.: Cambridge Univ. Press, 1996.
3. M. Vidyasagar, Nonlinear Systems Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1993.
4. L. Ljung, "Analysis of recursive stochastic algorithms," IEEE Trans. Automat. Contr., vol. AC-22, pp. 551-575, 1977.
5. H. J. Kushner and D. S. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems. New York: Springer-Verlag, 1978.
6. L. Xu, E. Oja, and C. Y. Suen, "Modified Hebbian learning for curve and surface fitting," Neural Networks, vol. 5, 1992, pp. 441-4157.
7. F.-L. Luo, R. Unbehauen, and Y.-D. Li, "A principal component analysis algorithm with invariant norm," Neurocomput., vol. 8, 1995, pp. 213-221.