Global convergence of a PCA learning algorithm with a constant learning rate

DOI10.1016/j.camwa.2006.08.028zbMath1138.62330OpenAlexW2081700245MaRDI QIDQ2469892

Zhang Yi, Jian Cheng Lv

Publication date: 11 February 2008

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2006.08.028

zbMATH Keywords

global convergenceneural networksprincipal component analysisdeterministic discrete time systemconstant learning rate

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Limit theorems in probability theory (60F99) Neural nets and related approaches to inference from stochastic processes (62M45)

Cites Work

• Adaptive algorithms for first principal eigenvector computation
• On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
• A simplified neuron model as a principal component analyzer
• Generalized neural networks for spectral analysis: dynamics and Liapunov functions
• Analysis of recursive stochastic algorithms

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