Simultaneous count data feature selection and clustering using Multinomial Nested Dirichlet Mixture

Simultaneous count data feature selection and clustering using Multinomial Nested Dirichlet Mixture

The elevating effect of the curse of dimensionality in count data has made clustering a challenging task. This paper solves this by adopting the concept of feature saliency as a feature selection method in the context of using the Multinomial Nested Dirichlet Mixture (MNDM). The MNDM is a generaliza...

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Bibliographic Details
Main Authors: Fares Alkhawaja, Manar Amayri, Nizar Bouguila
Format: Article
Language:English
Published: LibraryPress@UF 2024-05-01
Series:Proceedings of the International Florida Artificial Intelligence Research Society Conference
Subjects:
mixture model clustering
expectation-maximization (em)
feature saliences
feature selection
multinomial nested dirichlet mixture (mndm)
minimum message length (mml)
Online Access:https://journals.flvc.org/FLAIRS/article/view/135262
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