Jun 5, 2019 � Abstract:The essential task of tensor data analysis focuses on the tensor decomposition and the corresponding notion of rank.
Aug 15, 2020 � We show that any third-order tensor can stably be recovered from few linear noise measurements under some certain t-RIP conditions via the RTNNM model.
Rankrestricted RIP in the matrix case [11] can be extended to the tubal-rank-restricted RIP [34] in the tensor case, which will pave the theoretical foundation�...
In this paper, by introducing the notion of tensor singular value decomposition (t-SVD), we establish a regularized tensor nuclear norm minimization (RTNNM)�...
On the other hand, many variants of the restricted isometry property (RIP) have proven to be crucial frameworks and analysis tools for recovery of sparse�...
Aug 15, 2020 � Furthermore, we show that any third-order tensor X can stably be recovered from few linear noise measurements under some certain t-RIP�...
RIP-based performance guarantee for low-tubal-rank tensor recovery. https://doi.org/10.1016/j.cam.2020.112767 �. Journal: Journal of Computational and Applied�...
Nov 21, 2018 � The essential task of multi-dimensional data analysis focuses on the tensor de- composition and the corresponding notion of rank.
Furthermore, we show that any third-order tensor X can stably be recovered from few linear noise measurements under some certain t-RIP conditions via the RTNNM�...
In this paper, by introducing the notion of tensor singular value decomposition (t-SVD), we establish a regularized tensor nuclear norm minimization (RTNNM)�...