Efficiently updating and tracking the dominant kernel principal components
type: Item
instance of: scholarly article
title: Efficiently updating and tracking the dominant kernel principal components
zbMATH Open document ID: 1111.68101
author: L. Hoegaerts
author: Joos Vandewalle
author: Lieven De Lathauwer
author: Ivan Goethals
author: Johan A. K. Suykens
author: B. De Moor
published in: Neural Networks
publication date: 2007-03-27T00:00:00Z
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 5137791
zbMATH Keywords: dominant eigenspace
zbMATH Keywords: eigenvalues
zbMATH Keywords: updating
zbMATH Keywords: tracking
zbMATH Keywords: kernel gram matrix
zbMATH Keywords: prinicipal components
zbMATH Keywords: large scale data
describes a project that uses: UCI-ml
MaRDI profile type: Publication
full work available at URL: j.neunet.2006.09.012
OpenAlex ID: W2056321881
cites work: Sliding Window Adaptive SVD Algorithms
cites work: Q3121550
cites work: Updating the singular value decomposition
cites work: Q3359644
cites work: Sequential Karhunen-Loève basis extraction and its application to images
cites work: A Singular Value Decomposition Updating Algorithm for Subspace Tracking
cites work: An Expectation-Maximization Approach to Nonlinear Component Analysis
cites work: Kernel PCA for feature extraction and de-noising in nonlinear regression
cites work: Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
cites work: The use of kernel principal component analysis to model data distributions
cites work: Q4856771
cites work: Q3996207
Wikidata QID: Q38400424
DOI: 10.1016/J.NEUNET.2006.09.012
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description: scientific article; zbMATH DE number 5137791