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Activity Number: 307
Type: Invited
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: JCGS-Journal of Computational and Graphical Statistics
Abstract - #303574
Title: Multilevel Functional Principal Component Analysis for High-Dimensional Data
Author(s): Vadim Zipunnikov*+ and Brian Scott Caffo and Ciprian Crainiceanu
Companies: Johns Hopkins Bloomberg School of Public Health and The Johns Hopkins University and The Johns Hopkins University
Address: 615 N. Wolfe Street, Baltimore, MD, , USA
Keywords: SVD ; PCA ; fMRI ; high-dimensional data

I will talk about HD-MFPCA, a fast and scalable statistical method for the analysis of ultra high dimensional vectors that are observed at multiple visits. The method does not require loading the entire data set at once in the computer memory and instead uses only sequential access to data. This allows its deployment on low-resource computers where computations can be done in minutes on extremely large data sets. I will also discuss two recent extensions of HD-MFPCA, Longitudinal Functional PCA (LFPCA) and Structured Functional PCA (SFPCA). LFPCA targets observational studies that collect imaging data longitudinally on large cohorts of subjects. SFPCA can be viewed as a high dimensional counterpart of classical nested and classification models.

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