Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

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 …

The popular tensor train (TT) and tensor ring (TR) decompositions have achieved promising
results in science and engineering. However, TT and TR decompositions only establish an …

In this paper, we propose a general framework for tensor singular value decomposition
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from
the laborious model selection problem due to their high model sensitivity. In particular, for …

With the increasing availability of various sensor technologies, we now have access to large
amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be …

With the advance of acquisition techniques, plentiful higherorder tensor data sets are built
up in a great variety of fields such as computer vision, neuroscience, remote sensing and …

We propose a novel sparse tensor decomposition method, namely the tensor truncated
power method, that incorporates variable selection in the estimation of decomposition …

Tensor completion recovers missing components of multi-way data. The existing methods
use either the Tucker rank or the CANDECOMP/PARAFAC (CP) rank in low-rank tensor …

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image
and Video Processing shows you how robust subspace learning and tracking by …