Adaptive algorithms for first principal eigenvector computation

DOI10.1016/j.neunet.2004.11.004zbMath1076.65034OpenAlexW2039069492WikidataQ51634105 ScholiaQ51634105MaRDI QIDQ557636

Chanchal Chatterjee

Publication date: 30 June 2005

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2004.11.004

zbMATH Keywords

numerical examplesstochastic approximationprincipal component analysisgradient methodsstochastic convergenceadaptive iterative algorithmscorrelation matrix of a random vector sequencefirst principal eigenvector

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Applications of stochastic analysis (to PDEs, etc.) (60H30)

Related Items (8)

Global convergence of Oja's PCA learning algorithm with a non-zero-approaching adaptive learning rate ⋮ Adaptive multiple minor directions extraction in parallel using a PCA neural network ⋮ Online Principal Component Analysis in High Dimension: Which Algorithm to Choose? ⋮ Convergence proof of matrix dynamics for online linear discriminant analysis ⋮ Stability and chaos of LMSER PCA learning algorithm ⋮ Convergence analysis of Chauvin's PCA learning algorithm with a constant learning rate ⋮ Global convergence of a PCA learning algorithm with a constant learning rate ⋮ Linear Prediction Based Blind Source Extraction Algorithms in Practical Applications

Cites Work

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• On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
• Stochastic approximation methods for constrained and unconstrained systems
• A survey of conjugate gradient algorithms for solution of extreme eigen-problems of a symmetric matrix
• Analysis of recursive stochastic algorithms

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