Title: Generalizable Patterns in Neuroimaging:
How Many Principal Components?
Authors: Lars Kai Hansen, Jan Larsen Finn Årup Nielsen
Department of Mathematical Modeling - Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
Phone: +45 4525 3889,3923
Fax: +45 4587 2599
E-mail: lkhansen,jl@imm.dtu.dk
Stephen C. Strother
PET Imaging Service, VA Medical Center, and
Radiology+Neurology Depts.
University of Minnesota,
Minneapolis, Minnesota, USA
Egill Rostrup
Danish Center for Magnetic Resonance,
Hvidovre Hospital, Denmark
Robert Savoy
Massachusetts General Hospital,
Boston, USA
Claus Svarer, Olaf B. Paulson
Neurobiology Research Unit,
Rigshospitalet, Copenhagen, Denmark
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.
Techn. Report., submitted for publication, 1998.