Sparse Tensor Additive Regression

Understand

Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing.

• In this paper, we propose a sparse tensor additive regression (STAR) that models a scalar response as a flexible nonparametric function of tensor covariates.
• The proposed model effectively exploits the sparse and low-rank structures in the tensor additive regression.
• We formulate the parameter estimation as a non-convex optimization problem, and propose an efficient penalized alternating minimization algorithm.