Reconstructing Analytic Dinosaurs: Polynomial Eigenvalue Decomposition for Eigenvalues with Unmajorised Ground Truth
Schlecht SJ, Weiss S (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: IEEE
City/Town: New York City
Pages Range: 1287-1291
Conference Proceedings Title: 2024 32nd European Signal Processing Conference (EUSIPCO)
Event location: Lyon
DOI: 10.23919/EUSIPCO63174.2024.10715310
Abstract
This paper proposes a novel method for accurately estimating the ground truth analytic eigenvalues from estimated space-time covariance matrices, where the estimation process obscures any intersection of eigenvalues with probability one. The approach involves grouping sufficiently separated, bin-wise eigenvalues into segments that belong to analytic functions and then solves a permutation problem to align these segments. By leveraging an inverse partial discrete Fourier transform and a linear assignment algorithm, the proposed EigenBone method retrieves analytic eigenvalues efficiently and accurately. Experimental results demonstrate the effectiveness of this approach in accurately reconstructing eigenvalues from noisy estimates. Overall, the proposed method offers a robust solution for approximating analytic eigenvalues in scenarios where state-of-the-art methods may fail.
Authors with CRIS profile
Involved external institutions
United Kingdom (GB)
Finland (FI)How to cite
APA:
Schlecht, S.J., & Weiss, S. (2024). Reconstructing Analytic Dinosaurs: Polynomial Eigenvalue Decomposition for Eigenvalues with Unmajorised Ground Truth. In 2024 32nd European Signal Processing Conference (EUSIPCO) (pp. 1287-1291). Lyon, FR: New York City: IEEE.
MLA:
Schlecht, Sebastian J., and Stephan Weiss. "Reconstructing Analytic Dinosaurs: Polynomial Eigenvalue Decomposition for Eigenvalues with Unmajorised Ground Truth." Proceedings of the 2024 32nd European Signal Processing Conference (EUSIPCO), Lyon New York City: IEEE, 2024. 1287-1291.
BibTeX: Download