A hierarchical count data clustering based on Multinomial Nested Dirichlet Mixture using the Minorization-Maximization framework

Despite the widespread acceptance of mixture models among researchers, obtaining good results involves several challenges. In this paper, we address two main challenges: parameter estimation and data representation strategies. Expectation-Maximization (EM) is a widely used framework for parameter es...

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Detalhes bibliográficos

Principais autores:	Fares Alkhawaja, Manar Amayri, Nizar Bouguila
Formato:	Artigo
Idioma:	Inglês
Publicado em:	LibraryPress@UF 2024-05-01
coleção:	Proceedings of the International Florida Artificial Intelligence Research Society Conference
Assuntos:	hierarchical feature learning mixture model clustering minorization-maximization (mm) multinomial nested dirichlet mixture (mndm) minimum message length (mml) spatial pyramid matching (spm)
Acesso em linha:	https://journals.flvc.org/FLAIRS/article/view/135263
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