… -R CP tensor decomposition. In general cases, we will show that this tensor deconvolution
can be expressed as the rank-(J1, J2, J3) block tensor decomposition [15] with Toeplitz factor …

Low-rank Deconvolution (LRD) has appeared as a new multi-dimensional representation
model that enjoys important efficiency and flexibility properties. In this work we ask ourselves if …

… deconvolution problem over the space of Radon measures. A common approach to off-the-grid
deconvolution … semidefinite lifting, which has low-rank solutions. Our second contribution …

… underlying low-rank approximations, tensor networks … low-rank tensor network decompositions
and easy to interpret graphical representations of the mathematical operations on tensor …

… The current paper uncovers the possibility of efficiently and stably completing a low-CP-rank
tensor from partial and noisy entries. Perhaps somewhat unexpectedly, despite the high …

… Low-rank Deconvolution, a powerful framework for low-level … sparse coding and low-rank
approximation methods as in … convolved with a set of low-rank tensors. We show its advantages …

… ABSTRACT: Approximating a tensor by another of lower rank is in general an ill posed …
both existence and uniqueness of the best low rank approximation. We then show how these …

… for several low-rank tensor models. Specifically, in the Tucker low-rank tensor PCA or …
for the singular subspace of the parameter tensor based on the asymptotic distribution of an …

… ing of 2-D low rank is that it can only work in the presence of two-… tensor. To preserve the
structural information, we introduce the low-rank tensor recovery model to handle the tensor …

Low-rank Deconvolution (LRD) has been recently introduced as a new representation
model for multi-dimensional data. In this work we consider its use for tackling the problem of …