Personal tools
 

Dimension Reduction of Neuroimaging Data Analysis

— filed under:

Dan Shen, University of South Florida

What
  • Colloquium
When Fri, Feb 17, 2017
from 11:00 AM to 11:50 AM
Where Ritter Hall 323
Add event to calendar vCal
iCal

 

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. 

« August 2017 »
August
SuMoTuWeThFrSa
12345
6789101112
13141516171819
20212223242526
2728293031