On the non-detectability of spiked large random tensors
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Abstract (arXiv)
This paper addresses the detection of a low rank high-dimensional tensor corrupted by an additive complex Gaussian noise. In the asymptotic regime where all the dimensions of the tensor converge towards +∞ at the same rate, existing results devoted to rank 1 tensors are extended. It is proved that if a certain parameter depending on the low rank tensor is below a threshold, then the null hypothesis and the presence of the low rank tensor are undistinguishable hypotheses in the sense that no test performs better than a random choice.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.
2 papers.
- 2021A Random Matrix Perspective on Random Tensorscited by 12
- 2024A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximationcited by 3