Work

Tensor Regression with Applications in Neuroimaging Data Analysis

Year: 2013

Type: article

Abstract: Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.

Source: Journal of the American Statistical Association

DOI: 10.1080/01621459.2013.776499

Authors: Hua Zhou, Lexin Li, Hongtu Zhu

Institutions: North Carolina State University, University of North Carolina at Chapel Hill

Language: English

Topic: Tensor decomposition and applications

Subfield: Computational Mathematics

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institute of Mental Health, National Human Genome Research Institute, National Cancer Institute, National Center for Research Resources