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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2016

01.10.2016

Model-Based Clustering Based on Variational Learning of Hierarchical Infinite Beta-Liouville Mixture Models

verfasst von: Wentao Fan, Nizar Bouguila

Erschienen in: Neural Processing Letters | Ausgabe 2/2016

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Abstract

In this work, we develop a statistical framework for data clustering which uses hierarchical Dirichlet processes and Beta-Liouville distributions. The parameters of this framework are leaned using two variational Bayes approaches. The first one considers batch settings and the second one takes into account the dynamic nature of real data. Experimental results based on a challenging problem namely visual scenes categorization demonstrate the merits of the proposed framework.
Vorheriger Artikel A Complex-valued Encoding Bat Algorithm for Solving 0–1 Knapsack Problem
Nächster Artikel Hierarchical Parallel Genetic Optimization Fuzzy ARTMAP Ensemble

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Fußnoten
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Metadaten
Titel
Model-Based Clustering Based on Variational Learning of Hierarchical Infinite Beta-Liouville Mixture Models
verfasst von
Wentao Fan
Nizar Bouguila
Publikationsdatum
01.10.2016
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2016
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-015-9466-x

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