Xin (Henry) Zhang
Department of Statistics
Florida State University

Covariate-adjusted tensor classification in high-dimensions 

Multidimensional array data, also known as tensor, are now frequently arising in a wide range of fields including neuroimaging, genomics, business, and social sciences. These applications have motivated a fast developing literature on the statistical modeling of tensor data. In this talk, I will present some recent work on tensor classification models, to help us gaining some new insights about parsimonious modeling with targeted dimension reduction and variable selection, integrating the information from both the tensor predictor and the additional covariates to achieve accurate classification, improved algorithms utilizing tensor structures, and various applications.