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Abstract Details
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Activity Number:
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303
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #302270 |
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Title:
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Streaming Algorithms and Their Applications to HD-MFPCA Models
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Author(s):
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Vadim Zipunnikov*+ and Brian Caffo and Ciprian Crainiceanu
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Companies:
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The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University
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Address:
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School of Public Health, Baltimore, MD, 21205,
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Keywords:
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MFPCA ;
SVD ;
streaming ;
MRI
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Abstract:
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Multilevel Functional Principal Component Analysis for High Dimensional (HD-MFPCA) data combines powerful data compression techniques and statistical inference to decompose the observed data in population- and visit-specific means and subject-specific within and between level variability. However, HD-MFPCA is computationally restricted to the observational studies with a few thousands of observations. We will show how streaming algorithms can be used to overcome this restriction. The suggested algorithm accumulates the information in a streaming fashion resulting in a linear complexity with respect to the sample size. It allows to extend HD-MFPCA to very large samples.
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