Subset based least squares subspace regression in RKHS

Neurocomputing

Volumn 63, Issue SPEC. ISS., 2005, Pages 293-323

  Hoegaerts, Luc ^a   Suykens, Johan A K ^a   Vandewalle, J ^a   De Moor, B ^a  

^a Katholieke Universiteit Leuven   (Belgium)

Author keywords

Eigenspaces; Fixed size least squares support vector machine; Kernel canonical correlation analysis; Kernel methods; Kernel partial least squares; Kernel principal component analysis; Nystr m approximation; Reduced set methods; Regression; Reproducing kernel Hilbert space; Subspaces

Indexed keywords

DATA REDUCTION; OPTIMIZATION; POLYNOMIAL APPROXIMATION; PROBLEM SOLVING; REGRESSION ANALYSIS; SET THEORY; VECTORS;

DATA SETS; FEATURE APPROXIMATION; KERNEL BASED METHODS; PARTIAL LEAST SQUARES;

LEAST SQUARES APPROXIMATIONS;

ACCURACY; ARTICLE; COMPUTER MEMORY; CORRELATION ANALYSIS; DATA BASE; PRIORITY JOURNAL; REGRESSION ANALYSIS; SAMPLE; STATISTICAL MODEL; STATISTICAL PARAMETERS; TIME;

EID: 12144258144     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2004.04.013     Document Type: Article

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