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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2018

18.04.2018

Variational Bayesian Inference for Infinite Dirichlet Mixture Towards Accurate Data Categorization

verfasst von: Yuping Lai, Wenda He, Yuan Ping, Jinshuai Qu, Xiufeng Zhang

Erschienen in: Wireless Personal Communications | Ausgabe 3/2018

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Abstract

In this paper, we focus on a variational Bayesian learning approach to infinite Dirichlet mixture model (VarInDMM) which inherits the confirmed effectiveness of modeling proportional data from infinite Dirichlet mixture model. Based on the Dirichlet process mixture model, VarInDMM has an interpretation as a mixture model with a countably infinite number of components, and it is able to determine the optimal value of this number according to the observed data. By introducing an extended variational inference framework, we further obtain an analytically tractable solution to estimate the posterior distributions of the parameters for the mixture model. Experimental results on both synthetic and real data demonstrate its good performance on object categorization and text categorization.
Vorheriger Artikel Distributed and Adaptive Analog Coding for Video Broadcast in Wireless Cooperative System
Nächster Artikel Entropy with Local Binary Patterns for Efficient Iris Liveness Detection

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Metadaten
Titel
Variational Bayesian Inference for Infinite Dirichlet Mixture Towards Accurate Data Categorization
verfasst von
Yuping Lai
Wenda He
Yuan Ping
Jinshuai Qu
Xiufeng Zhang
Publikationsdatum
18.04.2018
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-018-5723-4

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