Efficiently updating and tracking the dominant kernel principal components

DOI10.1016/j.neunet.2006.09.012zbMath1111.68101OpenAlexW2056321881WikidataQ38400424 ScholiaQ38400424MaRDI QIDQ872404

L. Hoegaerts, Joos Vandewalle, Lieven De Lathauwer, Ivan Goethals, Johan A. K. Suykens, Bart De Moor

Publication date: 27 March 2007

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2006.09.012

zbMATH Keywords

trackingeigenvaluesupdatinglarge scale datadominant eigenspacekernel gram matrixprinicipal components

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Adaptive kernel canonical correlation analysis algorithms for nonparametric identification of Wiener and Hammerstein systems ⋮ Adaptive kernel principal component analysis ⋮ On-line versus off-line accelerated kernel feature analysis: application to computer-aided detection of polyps in CT colonography ⋮ Efficient Tracking of the Dominant Eigenspace of a Normalized Kernel Matrix

Uses Software

• UCI-ml

Cites Work

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• The use of kernel principal component analysis to model data distributions
• Sequential Karhunen-Loeve basis extraction and its application to images
• A Singular Value Decomposition Updating Algorithm for Subspace Tracking
• Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
• An Expectation-Maximization Approach to Nonlinear Component Analysis
• Sliding Window Adaptive SVD Algorithms
• Updating the singular value decomposition
• Kernel PCA for feature extraction and de-noising in nonlinear regression

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