Unsupervised linear component analysis for a class of probability mixture models - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Signal Processing Letters Année : 2023

Unsupervised linear component analysis for a class of probability mixture models

Résumé

We deal with a model where a set of observations is obtained by a linear superposition of unknown components called sources. The problem consists in recovering the sources without knowing the linear transform. We extend the well-known Independent Component Analysis (ICA) methodology. Instead of assuming independent source components, we assume that the source vector is a probability mixture of two distributions. Only one distribution satisfies the ICA assumptions, while the other one is concentrated on a specific but unknown support. Sample points from the latter are clustered based on a data-driven distance in a fully unsupervised approach. A theoretical grounding is provided through a link with the Christoffel function. Simulation results validate our approach and illustrate that it is an extension of a formerly proposed method.
Fichier principal
Vignette du fichier
main_spl.pdf (1.31 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04337530 , version 1 (12-12-2023)

Identifiants

Citer

Marc Castella. Unsupervised linear component analysis for a class of probability mixture models. IEEE Signal Processing Letters, 2023, pp.1-5. ⟨10.1109/lsp.2023.3341005⟩. ⟨hal-04337530⟩
69 Consultations
29 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More