Multi-dimensional visual data completion via low-rank tensor representation under coupled transform

JL Wang, TZ Huang, XL Zhao… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
This paper addresses the tensor completion problem, which aims to recover missing
information of multi-dimensional images. How to represent a low-rank structure embedded …

Modewise operators, the tensor restricted isometry property, and low-rank tensor recovery

CA Haselby, MA Iwen, D Needell, M Perlmutter… - Applied and …, 2023 - Elsevier
Recovery of sparse vectors and low-rank matrices from a small number of linear
measurements is well-known to be possible under various model assumptions on the …

Fast and Low-Memory Compressive Sensing Algorithms for Low Tucker-Rank Tensor Approximation from Streamed Measurements

C Haselby, MA Iwen, D Needell, E Rebrova… - arXiv preprint arXiv …, 2023 - arxiv.org
In this paper we consider the problem of recovering a low-rank Tucker approximation to a
massive tensor based solely on structured random compressive measurements. Crucially …

Modewise Operators, the Tensor Restricted Isometry Property, and Low-Rank Tensor Recovery

MA Iwen, D Needell, M Perlmutter… - arXiv preprint arXiv …, 2021 - arxiv.org
Recovery of sparse vectors and low-rank matrices from a small number of linear
measurements is well-known to be possible under various model assumptions on the …

[图书][B] On Methods in Tensor Recovery and Completion

C Haselby - 2023 - search.proquest.com
Tensor representations of data have great promise, since as the size of data grows both in
terms of dimensionality and modes, it becomes increasingly advantageous to employ …

[图书][B] Uniform Concentration of Tensor and Neural Networks: An Approach towards Recovery Guarantees

AC Goeßmann - 2021 - search.proquest.com
This thesis contributes to the uniform concentration approach towards guaranteeing the
generalization of learned models. We show probabilistic bounds on various uniform …