Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion
(LRTC) has achieved unprecedented success in addressing various pattern analysis issues …

Tensor robust principal component analysis (TRPCA) is a fundamental model in machine
learning and computer vision. Recently, tensor train (TT) decomposition has been verified …

Canonical Polyadic (CP) decomposition is a powerful multilinear algebra tool for analyzing
multiway (aka tensor) data and has been used for various signal processing and machine …

Aiming at recovering an unknown tensor (ie, multi-way array) corrupted by both sparse
outliers and dense noises, robust tensor decomposition (RTD) serves as a powerful pre …

Abstract Knowledge distillation (KD) has been proven an effective method in intelligent edge
computing and have achieved extensive study in recent deep learning research. However …

As a demonstrated and foremost approach of extracting the key features from corrupted
observations, tensor robust principal component analysis has been considered in various …

Generalized inverses of tensors play increasingly important roles in computational
mathematics and numerical analysis. It is appropriate to develop the theory of generalized …

In recent studies, the tensor ring (TR) rank has shown high effectiveness in tensor
completion due to its ability of capturing the intrinsic structure within high-order tensors. A …

Multi-channel learning has gained significant attention in recent applications, where neural
networks with t-product layers (t-NNs) have shown promising performance through novel …

Conventional Outlier Robust Tensor Decomposition (ORTD) approaches generally
represent sparse outlier corruption within a specific mode. However, such an assumption …