Fast subspace tracking and neural network learning by a novel information criterion
- 1 July 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 46 (7) , 1967-1979
- https://doi.org/10.1109/78.700968
Abstract
No abstract availableKeywords
This publication has 23 references indexed in Scilit:
- Global convergence of Oja's subspace algorithm for principal component extractionIEEE Transactions on Neural Networks, 1998
- Conjugate gradient eigenstructure tracking for adaptive spectral estimationIEEE Transactions on Signal Processing, 1995
- Principal component extraction using recursive least squares learningIEEE Transactions on Neural Networks, 1995
- Projection approximation subspace trackingIEEE Transactions on Signal Processing, 1995
- Adaptive estimation of eigensubspaceIEEE Transactions on Signal Processing, 1995
- Lyapunov functions for convergence of principal component algorithmsNeural Networks, 1995
- Generalizations of principal component analysis, optimization problems, and neural networksNeural Networks, 1995
- Adaptive Principal component EXtraction (APEX) and applicationsIEEE Transactions on Signal Processing, 1994
- Simplified neural networks for solving linear least squares and total least squares problems in real timeIEEE Transactions on Neural Networks, 1994
- On SVD for estimating generalized eigenvalues of singular matrix pencil in noiseIEEE Transactions on Signal Processing, 1991