Generalizable patterns in neuroimaging: how many principal components?
Research output: Contribution to journal › Journal article › Research › peer-review
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.
Original language | English |
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Journal | NeuroImage |
Volume | 9 |
Issue number | 5 |
Pages (from-to) | 534-44 |
Number of pages | 11 |
ISSN | 1053-8119 |
DOIs | |
Publication status | Published - May 1999 |
Bibliographical note
Copyright 1999 Academic Press.
- Algorithms, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging/methods, Models, Statistical, Normal Distribution, Photic Stimulation, Psychomotor Performance/physiology, Reproducibility of Results
Research areas
ID: 260210628