Tensor methods in computer vision and deep learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
Tensor completion algorithms in big data analytics
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 …
observed tensors. Due to the multidimensional character of tensors in describing complex …
Fully-connected tensor network decomposition and its application to higher-order tensor completion
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 …
results in science and engineering. However, TT and TR decompositions only establish an …
Tensor SVD: Statistical and computational limits
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 …
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …
Tensor ring decomposition with rank minimization on latent space: An efficient approach for tensor completion
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 …
the laborious model selection problem due to their high model sensitivity. In particular, for …
Linked component analysis from matrices to high-order tensors: Applications to biomedical data
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 …
amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be …
Low-rank tensor completion with total variation for visual data inpainting
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 …
up in a great variety of fields such as computer vision, neuroscience, remote sensing and …
Provable sparse tensor decomposition
We propose a novel sparse tensor decomposition method, namely the tensor truncated
power method, that incorporates variable selection in the estimation of decomposition …
power method, that incorporates variable selection in the estimation of decomposition …
Low CP rank and tucker rank tensor completion for estimating missing components in image data
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 …
use either the Tucker rank or the CANDECOMP/PARAFAC (CP) rank in low-rank tensor …
[图书][B] Handbook of robust low-rank and sparse matrix decomposition: Applications in image and video processing
Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image
and Video Processing shows you how robust subspace learning and tracking by …
and Video Processing shows you how robust subspace learning and tracking by …