Efficiently updating and tracking the dominant kernel principal components

Neural Networks

Volumn 20, Issue 2, 2007, Pages 220-229

  Hoegaerts, L ^a   De Lathauwer, L ^b   Goethals, I ^a   Suykens, J A K ^a   Vandewalle, J ^a   De Moor, B ^a  

^a Katholieke Universiteit Leuven   (Belgium)

^b Centre national de la recherche scientifique   (France)

Author keywords

Dominant eigenspace; Eigenvalues; Kernel Gram matrix; Large scale data; Prinicipal components; Tracking; Updating

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL METHODS; FEATURE EXTRACTION; ITERATIVE METHODS; LEARNING SYSTEMS; PRINCIPAL COMPONENT ANALYSIS;

BATCH ALGORITHM; EIGENBASIS; EIGENVECTOR DECOMPOSITION;

EIGENVALUES AND EIGENFUNCTIONS;

ARTICLE; COMPUTER MEMORY; KERNEL METHOD; MACHINE LEARNING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; SIGNAL PROCESSING; SUPPORT VECTOR MACHINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; INFORMATION STORAGE AND RETRIEVAL; LOGICAL OBSERVATION IDENTIFIERS NAMES AND CODES; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS;

EID: 33847205368     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2006.09.012     Document Type: Article

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