Tensor Regression with Applications in Neuroimaging Data Analysis.

TitleTensor Regression with Applications in Neuroimaging Data Analysis.
Publication TypeJournal Article
Year of Publication2013
AuthorsZhou, Hua, Lexin Li, and Hongtu Zhu
JournalJ Am Stat Assoc
Date Published2013

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.

Alternate JournalJ Am Stat Assoc
Original PublicationTensor regression with applications in neuroimaging data analysis.
PubMed ID24791032
PubMed Central IDPMC4004091
Grant ListR01 HG006139 / HG / NHGRI NIH HHS / United States
TL1 RR025745 / RR / NCRR NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
KL2 RR025746 / RR / NCRR NIH HHS / United States