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Generalizable patterns in neuroimaging: how many principal components?

L K Hansen·J Larsen·F A Nielsen·S C Strother·E Rostrup·R Savoy·N Lange·J Sidtis·C Svarer·O B Paulson
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Abstract

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.

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AlgorithmsHumansImage Processing, Computer-AssistedLikelihood FunctionsMagnetic Resonance ImagingModels, StatisticalNormal DistributionPhotic StimulationPsychomotor PerformanceReproducibility of ResultsClinical TrialJournal ArticleRandomized Controlled TrialResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

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BibTeXRIS

L K Hansen, J Larsen, F A Nielsen, S C Strother, E Rostrup, R Savoy, N Lange, J Sidtis, C Svarer, O B Paulson. 1999. Generalizable patterns in neuroimaging: how many principal components?. https://doi.org/10.1006/nimg.1998.0425

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