scholarly journals An orthogonal equivalence theorem for third order tensors

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Liqun Qi ◽  
Chen Ling ◽  
Jinjie Liu ◽  
Chen Ouyang

<p style='text-indent:20px;'>In 2011, Kilmer and Martin proposed tensor singular value decomposition (T-SVD) for third order tensors. Since then, T-SVD has applications in low rank tensor approximation, tensor recovery, multi-view clustering, multi-view feature extraction, tensor sketching, etc. By going through the Discrete Fourier Transform (DFT), matrix SVD and inverse DFT, a third order tensor is mapped to an f-diagonal third order tensor. We call this a Kilmer-Martin mapping. We show that the Kilmer-Martin mapping of a third order tensor is invariant if that third order tensor is taking T-product with some orthogonal tensors. We define singular values and T-rank of that third order tensor based upon its Kilmer-Martin mapping. Thus, tensor tubal rank, T-rank, singular values and T-singular values of a third order tensor are invariant when it is taking T-product with some orthogonal tensors. Some properties of singular values, T-rank and best T-rank one approximation are discussed.</p>

2014 ◽  
Vol 530-531 ◽  
pp. 581-585
Author(s):  
Jin Fang Cheng ◽  
Fu Qian ◽  
Nan Li

In this letter, we put forward a novel tensor-based Multiple Signal Classification (TB-MUSIC) applicable to a vector hydrophone array. For this purpose, the signal subspace is derived from the higher order singular value decomposition (HOSVD) of the third order tensor of the output model. Then the proposed method is achieved by signal subspace projection weighted with the reciprocal of principal singular values multiplying by the spatial spectrum based on TB-MUSIC. The synthetic spatial spectrum shows higher resolution and robustness under no-ideal scenarios. Monte Carlo experimental results are provided to illustrate the better performance of the proposed method.


2021 ◽  
pp. 108178
Author(s):  
Marouane Nazih ◽  
Khalid Minaoui ◽  
Elaheh Sobhani ◽  
Pierre Comon

Author(s):  
Qiang Jiang ◽  
Michael Ng

This paper considers the problem of recovering multidimensional array, in particular third-order tensor, from a random subset of its arbitrarily corrupted entries. Our study is based on a recently proposed algebraic framework in which the tensor-SVD is introduced to capture the low-tubal-rank structure in tensor. We analyze the performance of a convex program, which minimizes a weighted combination of the tensor nuclear norm, a convex surrogate for the tensor tubal rank, and the tensor l1 norm. We prove that under certain incoherence conditions, this program can recover the tensor exactly with overwhelming probability, provided that its tubal rank is not too large and that the corruptions are reasonably sparse. The number of required observations is order optimal (up to a logarithm factor) when comparing with the degrees of freedom of the low-tubal-rank tensor. Numerical experiments verify our theoretical results and real-world applications demonstrate the effectiveness of our algorithm.


2020 ◽  
Vol 29 ◽  
pp. 9044-9059
Author(s):  
Lin Chen ◽  
Xue Jiang ◽  
Xingzhao Liu ◽  
Zhixin Zhou

2020 ◽  
Vol 532 ◽  
pp. 170-189
Author(s):  
Yu-Bang Zheng ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tai-Xiang Jiang ◽  
Teng-Yu Ji ◽  
...  

2014 ◽  
Vol 35 (1) ◽  
pp. 225-253 ◽  
Author(s):  
Donald Goldfarb ◽  
Zhiwei (Tony) Qin

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