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Journal article

Generalizable Patterns in Neuroimaging: How Many Principal Components?

By Hansen, Lars Kai1,2; Larsen, Jan1,2; Nielsen, Finn Årup1,2; Strother, S.C.3; Rostrup, E.4; Savoy, R.5; Lange, N.6; Sidtis, J.J.; Svarer, C.7; Paulson, O.B.7

From

Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark1

Department of Informatics and Mathematical Modeling, Technical University of Denmark2

Minneapolis VA Medical Center3

Danish Research Centre for Magnetic Resonance4

Massachusetts General Hospital5

Massachusetts General Hospital/Harvard Medical School6

Rigshospitalet7

Generalization can be defined quantitatively and can be used to assess the performance of principal component analysis (PCA). The generalizability of PCA depends on the number of principal components retained in the analysis. We provide analytic and test set estimates of generalization. We show how the generalization error can be used to select the number of principal components in two analyses of functional magnetic resonance imaging activation sets.

Language: English
Year: 1999
Pages: 534-544
ISSN: 10959572 and 10538119
Types: Journal article
DOI: 10.1006/nimg.1998.0425
ORCIDs: 0000-0001-7712-8596 , Hansen, Lars Kai , Larsen, Jan and Nielsen, Finn Årup
Keywords

Algorithms Humans Image Processing, Computer-Assisted Likelihood Functions Magnetic Resonance Imaging Magnetic Resonance Imaging/methods Models, Statistical Normal Distribution Photic Stimulation Psychomotor Performance Psychomotor Performance/physiology Reproducibility of Results

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