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https://hdl.handle.net/20.500.14279/4107
Τίτλος: | A Spatially-constrained Normalized Gamma Process Prior | Συγγραφείς: | Korkinof, Dimitrios Demiris, Yiannis Chatzis, Sotirios P. |
Major Field of Science: | Engineering and Technology | Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering | Λέξεις-κλειδιά: | Computer science;Artificial intelligence;Expert systems (Computer science);Markov random fields | Ημερομηνία Έκδοσης: | 1-Δεκ-2012 | Πηγή: | Expert systems with applications, 2012, vol. 39, no. 17, pp. 13019–13025 | Volume: | 39 | Issue: | 17 | Start page: | 13019 | End page: | 13025 | Περιοδικό: | Expert systems with applications | Περίληψη: | 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 |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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