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Titolo:
Generalizable patterns in neuroimaging: How many principal components?
Autore:
Hansen, LK; Larsen, J; Nielsen, FA; Strother, SC; Rostrup, E; Savoy, R; Lange, N; Sidtis, J; Svarer, C; Paulson, OB;
Indirizzi:
Tech Univ Denmark, Dept Math Modeling, DK-2800 Lyngby, Denmark Tech Univ Denmark Lyngby Denmark DK-2800 deling, DK-2800 Lyngby, Denmark Univ Minnesota, Dept Neurol, Minneapolis, MN 55455 USA Univ Minnesota Minneapolis MN USA 55455 Neurol, Minneapolis, MN 55455 USA Univ Minnesota, Dept Radiol, Minneapolis, MN 55455 USA Univ Minnesota Minneapolis MN USA 55455 Radiol, Minneapolis, MN 55455 USA Univ5Minnesota, Vet Affairs Med Ctr, PET Imaging Serv, Minneapolis, MN 5545 Univ Minnesota Minneapolis MN USA 55455 maging Serv, Minneapolis, MN 5545 Univre,penhagen, Hvidovre Hosp, Danish Ctr Magnet Resonance, DK-2650 Hvidov Univ Copenhagen Hvidovre Denmark DK-2650 agnet Resonance, DK-2650 Hvidov Massachusetts Gen Hosp, Dept Radiol, Charlestown, MA 02139 USA Massachusetts Gen Hosp Charlestown MA USA 02139 Charlestown, MA 02139 USA Harvard Univ, Sch Med, Belmont, MA 02178 USA Harvard Univ Belmont MA USA 02178 rd Univ, Sch Med, Belmont, MA 02178 USA McLean Hosp, Belmont, MA 02178 USA McLean Hosp Belmont MA USA 02178McLean Hosp, Belmont, MA 02178 USA Rigshosp, Neurobiol Res Unit, DK-2100 Copenhagen, Denmark Rigshosp Copenhagen Denmark DK-2100 es Unit, DK-2100 Copenhagen, Denmark
Titolo Testata:
NEUROIMAGE
fascicolo: 5, volume: 9, anno: 1999,
pagine: 534 - 544
SICI:
1053-8119(199905)9:5<534:GPINHM>2.0.ZU;2-V
Fonte:
ISI
Lingua:
ENG
Soggetto:
FUNCTIONAL CONNECTIVITY; LINEAR-MODELS; PET; NETWORK; FMRI;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
30
Recensione:
Indirizzi per estratti:
Indirizzo: Hansen, LK Tech Univ Denmark, Dept Math Modeling, Bldg 321, DK-2800 Lyngby, Denmark Tech Univ Denmark Bldg 321 Lyngby Denmark DK-2800 gby, Denmark
Citazione:
L.K. Hansen et al., "Generalizable patterns in neuroimaging: How many principal components?", NEUROIMAGE, 9(5), 1999, pp. 534-544

Abstract

Generalization can be defined quantitatively and can be used to assess theperformance of principal component analysis (PCA). The generalizability ofPCA depends on the number of principal components retained in the analysis. We provide analytic and test set estimates of generalization. We show howthe generalization error can be used to select the number of principal components in two analyses of functional magnetic resonance imaging activationsets. (C) 1999 Academic Press.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 28/03/20 alle ore 23:22:48