Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2024

Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications

Résumé

This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA), specifically addressing the challenge of limited labeled signals in the target dataset. Leveraging a domain-dependent mixing model and the optimal transport domain adaptation assumption, we exploit abundant unlabeled data in the target domain to ensure effective prediction by establishing pairwise correspondence with equivalent signal variances between domains. Theoretical foundations are laid for identifying crucial Stiefel matrices, essential for recovering underlying signal variances from a Riemannian representation of observed signal covariances. We propose an integrated cost function that simultaneously learns these matrices, pairwise domain relationships, and a predictor, classifier, or regressor, depending on the task. Applied to neuroscience problems, MSA outperforms recent methods in brain-age regression with task variations using magnetoencephalography (MEG) signals from the Cam-CAN dataset.
Fichier principal
Vignette du fichier
Mixing_model_Stiefel_Adaptation_Arxiv.pdf (1.14 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04422094 , version 1 (28-01-2024)

Identifiants

  • HAL Id : hal-04422094 , version 1

Citer

Antoine Collas, Rémi Flamary, Alexandre Gramfort. Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications. 2024. ⟨hal-04422094⟩
65 Consultations
81 Téléchargements

Partager

Gmail Facebook X LinkedIn More