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Information Systems Frontiers
Erschienen in: Information Systems Frontiers 5/2020

21.07.2020

Online Variational Learning of Dirichlet Process Mixtures of Scaled Dirichlet Distributions

verfasst von: Narges Manouchehri, Hieu Nguyen, Pantea Koochemeshkian, Nizar Bouguila, Wentao Fan

Erschienen in: Information Systems Frontiers | Ausgabe 5/2020

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Abstract

Data clustering as an unsupervised method has been one of the main attention-grabbing techniques and a large class of tasks can be formulated by this method. Mixture models as a branch of clustering methods have been used in various fields of research such as computer vision and pattern recognition. To apply these models, we need to address some problems such as finding a proper distribution that properly fits data, defining model complexity and estimating the model parameters. In this paper, we apply scaled Dirichlet distribution to tackle the first challenge and propose a novel online variational method to mitigate the other two issues simultaneously. The effectiveness of the proposed work is evaluated by four challenging real applications, namely, text and image spam categorization, diabetes and hepatitis detection.
Vorheriger Artikel Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE
Nächster Artikel An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering

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Metadaten
Titel
Online Variational Learning of Dirichlet Process Mixtures of Scaled Dirichlet Distributions
verfasst von
Narges Manouchehri
Hieu Nguyen
Pantea Koochemeshkian
Nizar Bouguila
Wentao Fan
Publikationsdatum
21.07.2020
Verlag
Springer US
Erschienen in
Information Systems Frontiers / Ausgabe 5/2020
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI
https://doi.org/10.1007/s10796-020-10027-2

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