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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorKorkinof, Dimitrios-
dc.contributor.authorDemiris, Yiannis-
dc.contributor.authorChatzis, Sotirios P.-
dc.contributor.otherΧατζής, Σωτήριος Π.-
dc.date.accessioned2013-02-19T09:53:32Zen
dc.date.accessioned2013-05-17T10:38:38Z-
dc.date.accessioned2015-12-09T12:04:16Z-
dc.date.available2013-02-19T09:53:32Zen
dc.date.available2013-05-17T10:38:38Z-
dc.date.available2015-12-09T12:04:16Z-
dc.date.issued2012-
dc.identifier.citation8th IFIP WG 12.5 International Conference on Artificial intelligence applications and innovations, AIAI 2012, Halkidiki, Greece, September 27-30, pp. 337-346en_US
dc.identifier.isbn978-3-642-33408-5 (print)-
dc.identifier.isbn978-3-642-33409-2 (online)-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/4272-
dc.descriptionPart of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, vol. 381).en_US
dc.description.abstractIn 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 prioren_US
dc.language.isoenen_US
dc.rights© 2012 IFIP International Federation for Information Processingen_US
dc.subjectInformation systemsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectMarkov random fieldsen_US
dc.titleA spatially-constrained normalized gamma process for data clusteringen_US
dc.typeBook Chapteren_US
dc.collaborationImperial College Londonen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.reviewpeer reviewed-
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceIFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovationsen_US
dc.identifier.doi10.1007/978-3-642-33409-2_35en_US
dc.dept.handle123456789/134en
cut.common.academicyear2019-2020en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
item.cerifentitytypePublications-
item.openairetypebookPart-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4956-4013-
crisitem.author.parentorgFaculty of Engineering and Technology-
Εμφανίζεται στις συλλογές:Κεφάλαια βιβλίων/Book chapters
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