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arXiv · 2012.10249

Reduced-Rank Tensor-on-Tensor Regression and Tensor-variate Analysis of Variance

Carlos Llosa-Vite·Ranjan Maitra
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Abstract

Fitting regression models with many multivariate responses and covariates can be challenging, but such responses and covariates sometimes have tensor-variate structure. We extend the classical multivariate regression model to exploit such structure in two ways: first, we impose four types of low-rank tensor formats on the regression coefficients. Second, we model the errors using the tensor-variate normal distribution that imposes a Kronecker separable format on the covariance matrix. We obtain maximum likelihood estimators via block-relaxation algorithms and derive their computational complexity and asymptotic distributions. Our regression framework enables us to formulate tensor-variate analysis of variance (TANOVA) methodology. This methodology, when applied in a one-way TANOVA layout, enables us to identify cerebral regions significantly associated with the interaction of suicide attempters or non-attemptor ideators and positive-, negative- or death-connoting words in a functional Magnetic Resonance Imaging study. Another application uses three-way TANOVA on the Labeled Faces in the Wild image dataset to distinguish facial characteristics related to ethnic origin, age group and gender.

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Carlos Llosa-Vite, Ranjan Maitra. 2020-12-18. Reduced-Rank Tensor-on-Tensor Regression and Tensor-variate Analysis of Variance. https://doi.org/10.1109/tpami.2022.3164836

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