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Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling

Ma, Z., Lai, Y., Kleijn, W.B., Song, Yi-Zhe, Wang, L. and Guo, J. (2019) Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling IEEE Transactions on Neural Networks and Learning Systems, 30 (2). pp. 449-463.

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Abstract

In this paper, we develop a novel variational Bayesian learning method for the Dirichlet process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very flexible for modeling vectors with positive elements. The recently proposed extended variational inference (EVI) framework is adopted to derive an analytically tractable solution. The convergency of the proposed algorithm is theoretically guaranteed by introducing single lower bound approximation to the original objective function in the EVI framework. In principle, the proposed model can be viewed as an infinite inverted Dirichlet mixture model that allows the automatic determination of the number of mixture components from data. Therefore, the problem of predetermining the optimal number of mixing components has been overcome. Moreover, the problems of overfitting and underfitting are avoided by the Bayesian estimation approach. Compared with several recently proposed DP-related methods and conventional applied methods, the good performance and effectiveness of the proposed method have been demonstrated with both synthesized data and real data evaluations.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Ma, Z.
Lai, Y.
Kleijn, W.B.
Song, Yi-Zhey.song@surrey.ac.uk
Wang, L.
Guo, J.
Date : February 2019
DOI : 10.1109/TNNLS.2018.2844399
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Bayesian estimation; Computer vision; Dirichlet process (DP) mixture; Inverted Dirichlet distribution; Variational learning
Depositing User : Clive Harris
Date Deposited : 28 Jun 2019 13:59
Last Modified : 28 Jun 2019 13:59
URI: http://epubs.surrey.ac.uk/id/eprint/852102

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