Tensor Completion

Tensor completion estimates unobserved entries in multiway data such as videos, hyperspectral images, traffic records, and biomedical measurements. Classical approaches use Tucker or tensor-ring factorization, t-SVD and tubal-rank models, or tensor nuclear-norm penalties, often optimized with ADMM; robust variants also model sparse noise and smoothness. Neural Tucker models, convolutional experts with attention, continuous neural representations, and coupled-tensor methods capture nonlinear patterns or use side information. A central challenge is balancing accuracy and recovery guarantees with computational cost, sparse or corrupted observations, and the preservation of fine detail across boundaries and data modalities.