Fundamentals of digital image processing
Fundamentals of digital image processing
Stability of Oja's PCA subspace rule
Neural Computation
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Random Iterative Models
Handwritten Digit Recognition by Local Principal Components Analysis
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Pattern Recognition by Invariant Reference Points
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Local Subspace Method for Pattern Recognition
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
An automatic system for model-based coding of faces
DCC '95 Proceedings of the Conference on Data Compression
Principal component extraction using recursive least squares learning
IEEE Transactions on Neural Networks
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It is shown that the discrete time dynamical system defined by the Oja-RLS algorithm is stable in the closed ring K(0,9/8) - $$\overline{K}$$(0,8/9) if only the initial gain β 0 is bounded by (2B) −1, where B = b 2 and b is the bound for the learning sequence. It is rigorously proved that automatically computed gains β n in Oja-RLS scheme converge to zero with the rate 1/n, almost surely.