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Dimension Reduction of Neuroimaging Data Analysis

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Dan Shen, University of South Florida

  • Colloquium
When Fri, Feb 17, 2017
from 11:00 AM to 11:50 AM
Where Ritter Hall 323
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Abstract:  High dimensionality has become a common feature of "big data” encountered in many divergent fields, such as neuroimaging and genetic analysis, which provides modern challenges for statistical analysis. To cope with the high dimensionality, dimension reduction becomes necessary. Principal component analysis (PCA) is arguably the most popular classical dimension reduction technique, which uses a few principal components (PCs) to explain most of the data variation.

We introduce Multiscale Weighted Principal Component Regression (MWPCR), a new variation of PCA, for neuroimaging analysis. MWPCR introduces two sets of novel weights, including global and local spatial weights, to enable a selective treatment of individual features and incorporation of class label information as well as spatial pattern within neuroimaging data. Simulation studies and real data analysis show that MWPCR outperforms several competing methods. 

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