Communication Dans Un Congrès Année : 2024

Robust Low-Rank Correlation Fitting

Résumé

This paper considers the problem of obtaining a low-rank factorization of a given correlation matrix. In order to handle possible spurious correlation coefficients within the input, a robust formulation is proposed with a criterion based on the Huber loss function. Minimizing this fitting criterion under the low-rank correlation structure constraint is then addressed using the block majorization-minimization framework. Several algorithm options are explored and compared in terms of computational complexity. The merits of the proposed correlation fitting method are then validated on simulations, and for the process of dimension reduction of microarray data.

Fichier principal
Vignette du fichier
ICASSP_2024.pdf (327.64 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04642652 , version 1 (10-07-2024)

Licence

Identifiants

Citer

Thu Ha Phi, Alexandre Hippert-Ferrer, Florent Bouchard, Arnaud Breloy. Robust Low-Rank Correlation Fitting. International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024), Apr 2024, Séoul, South Korea. pp.9416-9420, ⟨10.1109/ICASSP48485.2024.10446634⟩. ⟨hal-04642652⟩
307 Consultations
659 Téléchargements

Altmetric

Partager

  • More