Tensor Restricted Isometry Property Analysis For a Large Class of Random Measurement Ensembles
Abstract
In previous work, theoretical analysis based on the tensor Restricted Isometry Property (t-RIP) established the robust recovery guarantees of a low-tubal-rank tensor. The obtained sufficient conditions depend strongly on the assumption that the linear measurement maps satisfy the t-RIP. In this paper, by exploiting the probabilistic arguments, we prove that such linear measurement maps exist under suitable conditions on the number of measurements in terms of the tubal rank r and the size of third-order tensor n1, n2, n3. And the obtained minimal possible number of linear measurements is nearly optimal compared with the degrees of freedom of a tensor with tubal rank r. Specially, we consider a random sub-Gaussian distribution that includes Gaussian, Bernoulli and all bounded distributions and construct a large class of linear maps that satisfy a t-RIP with high probability. Moreover, the validity of the required number of measurements is verified by numerical experiments.
Keywords
Cite
@article{arxiv.1906.01198,
title = {Tensor Restricted Isometry Property Analysis For a Large Class of Random Measurement Ensembles},
author = {Feng Zhang and Wendong Wang and Jingyao Hou and Jianjun Wang and Jianwen Huang},
journal= {arXiv preprint arXiv:1906.01198},
year = {2019}
}