Title: Efficiently updating and tracking the dominant kernel principal components
Authors: Hoegaerts, Luc ×
De Lathauwer, Lieven
Goethals, I
Suykens, Johan
Vandewalle, Joos
De Moor, Bart #
Issue Date: Mar-2007
Publisher: Pergamon
Series Title: Neural Networks vol:20 issue:2 pages:220-229
Abstract: The dominant set of eigenvectors of the symmetrical kernel Gram matrix is used in many important kernel methods (like e.g. kernel Principal Component Analysis, feature approximation, denoising, compression, prediction) in the machine learning area. Yet in the case of dynamic and/or large-scale data, the batch calculation nature and computational demands of the eigenvector decomposition limit these methods in numerous applications. In this paper we present an efficient incremental approach for fast calculation of the dominant kernel eigenbasis, which allows us to track the kernel eigenspace dynamically. Experiments show that our updating scheme delivers a numerically stable and accurate approximation for eigenvalues and eigenvectors at every iteration in comparison to the batch algorithm. (c) 2006 Elsevier Ltd. All rights reserved.
ISSN: 0893-6080
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Electrical Engineering - miscellaneous
Faculty of Science, Campus Kulak Kortrijk
× corresponding author
# (joint) last author

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