Xiao-Yu Li

STOD: Sparse Tensor Train Optimization via Orthogonal Decomposition for High-Dimensional Learning

This paper proposes a novel Tensor Train (TT)-based tensor-on-tensor regression optimization framework for variable selection based on mode-1 hyperslice sparsity, and designs an alternating iterative algorithm equipped with a preconditioned metric to efficiently solve the proposed model.