FUNCTIONAL PRINCIPAL COMPONENTS MODEL FOR HIGH-DIMENSIONAL BRAIN IMAGINGJohns Hopkins University, Dept. of Biostatistics Working Papers
Date of this Version1-18-2011
AbstractWe establish a fundamental equivalence between singular value decomposition (SVD) and functional principal components analysis (FPCA) models. The constructive relationship allows to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a functional mixed effect model is fitted to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed.
Citation InformationVadim Zipunnikov, Brian S Caffo, David M. Yousem, Christos Davatzikos, et al.. "FUNCTIONAL PRINCIPAL COMPONENTS MODEL FOR HIGH-DIMENSIONAL BRAIN IMAGING" (2011)
Available at: http://works.bepress.com/ciprian_crainiceanu/17/