Tucker Tensor Regression and Neuroimaging Analysis

Xiaoshan Li, Da Xu, Hua Zhou and Lexin Li ()
Additional contact information
Xiaoshan Li: Wells Fargo & Company
Da Xu: University of California
Hua Zhou: University of California
Lexin Li: University of California

Statistics in Biosciences, 2018, vol. 10, issue 3, No 3, 520-545

Abstract: Abstract Neuroimaging data often take the form of high-dimensional arrays, also known as tensors. Addressing scientific questions arising from such data demands new regression models that take multidimensional arrays as covariates. Simply turning an image array into a vector would both cause extremely high dimensionality and destroy the inherent spatial structure of the array. In a recent work, Zhou et al. (J Am Stat Assoc, 108(502):540–552, 2013) proposed a family of generalized linear tensor regression models based upon the CP (CANDECOMP/PARAFAC) decomposition of regression coefficient array. Low-rank approximation brings the ultrahigh dimensionality to a manageable level and leads to efficient estimation. In this article, we propose a tensor regression model based on the more flexible Tucker decomposition. Compared to the CP model, Tucker regression model allows different number of factors along each mode. Such flexibility leads to several advantages that are particularly suited to neuroimaging analysis, including further reduction of the number of free parameters, accommodation of images with skewed dimensions, explicit modeling of interactions, and a principled way of image downsizing. We also compare the Tucker model with CP numerically on both simulated data and real magnetic resonance imaging data, and demonstrate its effectiveness in finite sample performance.

Keywords: CP decomposition; Magnetic resonance image; Multidimensional array; Tucker decomposition; Tensor regression (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s12561-018-9215-6

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