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Activity Number: 603 - New Development on Statistics in Imaging
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Imaging
Abstract #306853
Title: Correlation Tensor Decomposition and Its Application in Spatial Imaging Data
Author(s): Yujia Deng* and Xiwei Tang and Annie Qu
Companies: University of Illinois Urbana and Champaign and University of Virginia and University of Illinois at Urbana-Champaign
Keywords: Multidimensional data; Spatial correlation; Tensor decomposition; Dimension reduction; Image processing
Abstract:

In this talk, we propose a new method to analyze spatial-correlated imaging data. In contrast to the conventional multivariate analysis where the variables are treated as vectors and correlation is represented as a matrix form, we formulate spatial correlation based on the tensor decomposition to preserve the spatial information of imaging data. Specifically, we propose an innovative algorithm to decompose the spatial correlation into a sum of rank-1 tensor such that the structure of the spatial information can be captured more fully compared to traditional approaches. Our method is effective in reducing the dimension of spatial correlated data, which is advantage in computation. In addition, we show that the proposed method can test against the null hypothesis of independent structure, and identifies the block patterns of spatial correlations of imaging data effectively and efficiently. We compare the proposed method with other competing methods through simulations and optical image data to detect early-stage breast cancer.


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

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