High-Resolution Subspace-Based Methods: Eigenvalue- or Eigenvector-Based Estimation?

Konstantin Usevich 1 Souleymen Sahnoun 1 Pierre Comon 1
1 GIPSA-CICS - CICS
GIPSA-DIS - Département Images et Signal
Abstract : In subspace-based methods for mulditimensional harmonic retrieval, the modes can be estimated either from eigenvalues or eigen-vectors. The purpose of this study is to find out which way is the best. We compare the state-of-the art methods N-D ESPRIT and IMDF, propose a modification of IMDF based on least-squares criterion, and derive expressions of the first-order perturbations for these methods. The theoretical expressions are confirmed by the computer experiments.
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Petr Tichavský, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadège Thirion-Moreau. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA-ICA 2017), Feb 2017, Grenoble, France. Springer, Lecture Notes in Computer Science 10169, pp.47-56, 2017, Latent Variable Analysis and Signal Separation 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings. <http://www.lva-ica-2017.com/>. <10.1007/978-3-319-53547-0_5>
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https://hal.archives-ouvertes.fr/hal-01576895
Contributeur : Pierre Comon <>
Soumis le : jeudi 24 août 2017 - 11:54:53
Dernière modification le : vendredi 8 septembre 2017 - 16:04:03

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Konstantin Usevich, Souleymen Sahnoun, Pierre Comon. High-Resolution Subspace-Based Methods: Eigenvalue- or Eigenvector-Based Estimation?. Petr Tichavský, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadège Thirion-Moreau. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA-ICA 2017), Feb 2017, Grenoble, France. Springer, Lecture Notes in Computer Science 10169, pp.47-56, 2017, Latent Variable Analysis and Signal Separation 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings. <http://www.lva-ica-2017.com/>. <10.1007/978-3-319-53547-0_5>. <hal-01576895>

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