Online Variational Learning of Dirichlet Process Mixtures of Scaled Dirichlet Distributions

Narges Manouchehri (), Hieu Nguyen, Pantea Koochemeshkian (), Nizar Bouguila () and Wentao Fan ()
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Narges Manouchehri: Concordia University
Hieu Nguyen: Concordia University
Pantea Koochemeshkian: Concordia University
Nizar Bouguila: Concordia University
Wentao Fan: Huaqiao University

Information Systems Frontiers, 2020, vol. 22, issue 5, No 7, 1085-1093

Abstract: 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.

Keywords: Infinite mixture models; Dirichlet process mixtures of scaled Dirichlet distributions; Online variational learning; Spam categorization; Diabetes; Hepatitis. (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-020-10027-2

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