Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

Low-rank tensor representations can provide highly compressed approximations of
functions. These concepts, which essentially amount to generalizations of classical …

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

This paper studies the Tensor Robust Principal Component (TRPCA) problem which
extends the known Robust PCA to the tensor case. Our model is based on a new tensor …

The widespread use of multisensor technology and the emergence of big data sets have
highlighted the limitations of standard flat-view matrix models and the necessity to move …

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor
completion problem, which has achieved state-of-the-art performance on image and video …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful
technique for tensor completion through explicitly capturing the multilinear latent factors. The …

Tensor recovery is a fundamental problem in tensor research field. It generally requires to
explore intrinsic prior structures underlying tensor data, and formulate them as certain forms …

The development of energy selective, photon counting X-ray detectors allows for a wide
range of new possibilities in the area of computed tomographic image formation. Under the …