Online Program Home
My Program

Abstract Details

Activity Number: 659
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Imaging
Abstract #319479
Title: Efficient Dimension Reduction for a Group of Images
Author(s): Dong Wang* and Haipeng Shen and Young Truong
Companies: and The University of Hong Kong and The University of North Carolina at Chapel Hill
Keywords: Dimension reduction ; Singular value decomposition ; Principal component analysis

Collection of a group of images is becoming more and more common. In this talk, we propose an efficient dimension reduction approach for a group of two-dimensional images. Each image is modeled by a product of three terms, two common group-level components and a subject-level one, and an additive noise term. The components are estimated via a two-step singular value decomposition (SVD) approach. The first step SVDs are applied on each image and the second one on the aggregated data. We demonstrate the superior performance of this approach through simulations and a real data example.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association