Robust Multi-Dimensional Model Order Estimation Using LineAr Regression of Global Eigenvalues (LaRGE)

@article{Korobkov2021RobustMM,
  title={Robust Multi-Dimensional Model Order Estimation Using LineAr Regression of Global Eigenvalues (LaRGE)},
  author={Alexey Alexandrovich Korobkov and Marina K. Diugurova and Jens Haueisen and Martin Haardt},
  journal={IEEE Transactions on Signal Processing},
  year={2021},
  volume={70},
  pages={5751-5764}
}
The efficient estimation of an approximate model order is very important for real applications with multi-dimensional low-rank data that may be corrupted by additive noise. In this paper, we present a novel robust to noise method for model order estimation of noise-corrupted multi-dimensional low-rank data based on the LineAr Regression of Global Eigenvalues (LaRGE). The LaRGE method uses the multi-linear singular values obtained from the HOSVD of the measurement tensor to construct global… 

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