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Anisotropic local covariance matrices for spatial blind source separation

Christoph Muehlmann et al.

AStA Advances in Statistical Analysis2025https://doi.org/10.1007/s10182-025-00529-2article
ABDC C
Weight
0.41

What the paper says

This paper aims to introduce a novel approach to spatial blind source separation (SBSS) that addresses the limitations of existing methods. Current SBSS techniques rely on the joint diagonalization of multiple local covariance functions, all of which assume isotropy. To overcome this constraint, anisotropic local covariance matrices that relax the isotropy assumption are proposed. A simulation study and an application on real-world data demonstrate the performance improvement obtained by incorporating these anisotropic covariance matrices into the SBSS framework and highlight the potential of this new approach for more accurate and flexible source separation in spatial data analysis.

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https://doi.org/https://doi.org/10.1007/s10182-025-00529-2

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@article{christoph2025,
  title        = {{Anisotropic local covariance matrices for spatial blind source separation}},
  author       = {Christoph Muehlmann et al.},
  journal      = {AStA Advances in Statistical Analysis},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s10182-025-00529-2},
}

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Anisotropic local covariance matrices for spatial blind source separation

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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