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Unpublished Paper
MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR HIGH-DIMENSIONAL DATA
Johns Hopkins University, Dept. of Biostatistics Working Papers
  • Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • Brian Caffo, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • Ciprian Crainiceanu, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • David M. Yousem, Johns Hopkins Hospital, Department of Radiology
  • Christos Davatzikos, University of Pennsylvania, Department of Radiology
  • Brian S. Schwartz, Johns Hopkins Bloomberg School of Public Health, Departments of Environmental Health Sciences and Epidemiology, and Medicine
Date of this Version
10-6-2010
Abstract
We propose fast and scalable statistical methods for the analysis of hundreds or thousands of high dimensional vectors observed at multiple visits. The proposed inferential methods avoid the difficult task of loading the entire data set at once in the computer memory and use sequential access to data. This allows deployment of our methodology on low-resource computers where computations can be done in minutes on extremely large data sets. Our methods are motivated by and applied to a study where hundreds of subjects were scanned using Magnetic Resonance Imaging (MRI) at two visits roughly five years apart. The original data possesses over ten billion easurements. The approach can be applied to any type of study where data can be unfolded into a long vector including densely bserved functions and images.
Citation Information
Vadim Zipunnikov, Brian Caffo, Ciprian Crainiceanu, David M. Yousem, et al.. "MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR HIGH-DIMENSIONAL DATA" (2010)
Available at: http://works.bepress.com/ciprian_crainiceanu/28/