It is shown that the unknown tensor Z ∈ Rn1�n2�n3 of tubal-rank r can be reconstructed as a unique solution of a tractable method — tensor nuclear norm (TNN) minimization, provided that the number of Gaussian observations m ≥ 3r(n1 + n2 − r)n3 + 1.
Mar 30, 2020 � In this work, we examine the fundamental question of the minimal number of linear observations needed to reconstruct the tensor Z from these�...
A tensor nuclear norm (TNN) based method for solving the tensor recovery problem was recently proposed, and it has achieved state-of-the-art performance.
In this work, we examine the fundamental question of the minimal number of linear observations needed to reconstruct the tensor $\boldsymbol{\mathcal {Z}}$ from�...
In this work, we examine the fundamental question of the minimal number of linear observations needed to reconstruct the tensor Z from these observations,�...
Uniqueness guarantee of solutions of tensor tubal-rank minimization problem. F Zhang, J Hou, J Wang, W Wang. IEEE Signal Processing Letters 27, 540-544, 2020.
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What is tubal rank of a tensor?
Why do we care about tensors?
The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints.
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.
From the perspective of atomic norm minimization, we give the low tubal rank recovery guarantee from Gaussian measurements. Specifically, to recover a tensor of�...
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Jun 16, 2024 � We have intensively tested and validated the effectiveness of the t-CCS model and the ITCURTC algorithm across both synthetic and real-world�...