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|Title:||A spatially-constrained normalized gamma process for data clustering||Authors:||Korkinof, Dimitrios
Chatzis, Sotirios P.
|Keywords:||Information systems;Artificial intelligence;Markov random fields||Category:||Electrical Engineering - Electronic Engineering - Information Engineering||Field:||Engineering and Technology||Issue Date:||2012||Publisher:||Springer||Source:||8th IFIP WG 12.5 International Conference on Artificial intelligence applications and innovations, AIAI 2012, Halkidiki, Greece, September 27-30, pp. 337-346||Conference:||IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations||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||Description:||Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, vol. 381).||URI:||http://ktisis.cut.ac.cy/handle/10488/7205||ISBN:||978-3-642-33408-5 (print)
|DOI:||10.1007/978-3-642-33409-2_35||Rights:||© 2012 IFIP International Federation for Information Processing||Type:||Book Chapter|
|Appears in Collections:||Κεφάλαια βιβλίων/Book chapters|
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