Proportional data modeling via entropy-based variational bayes learning of mixture models

Proportional data modeling via entropy-based variational bayes learning of mixture models During the last few decades, many statistical approaches that were developed in the fields of computer vision and pattern recognition are based on mixture models. A mixture-based representation has a number of advantages: mixture models are generative, flexible, plus they can take prior information into account to improve the generalization capability. The mixture models that we consider in this paper are based on the Dirichlet and generalized Dirichlet distributions that have been widely used to represent proportional data. The novel aspect of this paper is to develop an entropy-based framework to learn these mixture models. Specifically, we propose a Bayesian framework for model learning by means of a sophisticated entropy-based variational Bayes technique. We present experimental results to show that the proposed method is effective in several applications namely person identity verification, 3D object recognition, text document clustering, and gene expression categorization. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Proportional data modeling via entropy-based variational bayes learning of mixture models

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
ISSN
0924-669X
eISSN
1573-7497
D.O.I.
10.1007/s10489-017-0909-0
Publisher site
See Article on Publisher Site

Abstract

During the last few decades, many statistical approaches that were developed in the fields of computer vision and pattern recognition are based on mixture models. A mixture-based representation has a number of advantages: mixture models are generative, flexible, plus they can take prior information into account to improve the generalization capability. The mixture models that we consider in this paper are based on the Dirichlet and generalized Dirichlet distributions that have been widely used to represent proportional data. The novel aspect of this paper is to develop an entropy-based framework to learn these mixture models. Specifically, we propose a Bayesian framework for model learning by means of a sophisticated entropy-based variational Bayes technique. We present experimental results to show that the proposed method is effective in several applications namely person identity verification, 3D object recognition, text document clustering, and gene expression categorization.

Journal

Applied IntelligenceSpringer Journals

Published: Apr 8, 2017

References

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