Approximate Joint Diagonalization According to the Natural Riemannian Distance - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Approximate Joint Diagonalization According to the Natural Riemannian Distance

Résumé

In this paper, we propose for the first time an approximate joint diagonalization (AJD) method based on the natural Riemannian distance of Hermitian positive definite matrices. We turn the AJD problem into an optimization problem with a Riemannian criterion and we developp a framework to optimize it. The originality of this criterion arises from the diagonal form it targets. We compare the performance of our Riemannian criterion to the classical ones based on the Frobe-nius norm and the log-det divergence, on both simulated data and real electroencephalographic (EEG) signals. Simulated data show that the Riemannian criterion is more accurate and allows faster convergence in terms of iterations. It also performs well on real data, suggesting that this new approach may be useful in other practical applications.
Fichier principal
Vignette du fichier
2017-LVA-ICA-cost_Rdist.pdf (483.17 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01551813 , version 1 (30-06-2017)

Identifiants

Citer

Florent Bouchard, Jérôme Malick, Marco Congedo. Approximate Joint Diagonalization According to the Natural Riemannian Distance. LVA/ICA 2017 - 13th International Conference on Latent Variable Analysis and Signal Separation, Feb 2017, Grenoble, France. pp.290-299, ⟨10.1007/978-3-319-53547-0_28⟩. ⟨hal-01551813⟩
163 Consultations
325 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More