A holistic similarity measure for GMMs by embedding it into the manifold of SPD matrices

In this paper, a new similarity measure for comparing two Gaussian Mixture Models (GMMs) is obtained. This is based on an embedding of the manifold of K-component GMMs into the manifold of the symmetric positive definite matrices (SPD). The GMM manifold with the pullback of the induced metric is sho...

Descripció completa

Dades bibliogràfiques

Autors principals:	Amit Vishwakarma, K. S. Subrahamanian Moosath
Format:	Artigo
Idioma:	Inglês
Publicat:	Taylor & Francis Group 2026-12-01
Col·lecció:	Applied Mathematics in Science and Engineering
Matèries:	Gaussian mixture models symmetric positive definite matrices statistical manifold divergence information geometry 53B12, 62B11
Accés en línia:	https://www.tandfonline.com/doi/10.1080/27690911.2026.2629491
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!