Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/4107
Title: A Spatially-constrained Normalized Gamma Process Prior
Authors: Korkinof, Dimitrios 
Demiris, Yiannis 
Chatzis, Sotirios P. 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Computer science;Artificial intelligence;Expert systems (Computer science);Markov random fields
Issue Date: 1-Dec-2012
Source: Expert systems with applications, 2012, vol. 39, no. 17, pp. 13019–13025
Volume: 39
Issue: 17
Start page: 13019
End page: 13025
Journal: Expert systems with applications 
Abstract: In this work, we propose a novel nonparametric Bayesian method for clustering of data with spatial interdependencies. Specifically, we devise a novel normalized Gamma process, regulated by a simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. As a result of its construction, the proposed model allows for introducing spatial dependencies in the clustering mechanics of the normalized Gamma process, thus yielding a novel nonparametric Bayesian method for spatial data clustering. We derive an efficient truncated variational Bayesian algorithm for model inference. We examine the efficacy of our approach by considering an image segmentation application using a real-world dataset. We show that our approach outperforms related methods from the field of Bayesian nonparametrics, including the infinite hidden Markov random field model, and the Dirichlet process prior
URI: https://hdl.handle.net/20.500.14279/4107
ISSN: 09574174
DOI: 10.1016/j.eswa.2012.05.097
Rights: © 2012 Elsevier.
Type: Article
Affiliation : Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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