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dc.contributor.authorArı, Çağlaren_US
dc.contributor.authorAksoy, Selimen_US
dc.coverage.spatialIstanbul, Turkey
dc.date.accessioned2016-02-08T12:22:38Z
dc.date.available2016-02-08T12:22:38Z
dc.date.issued2010-08en_US
dc.identifier.urihttp://hdl.handle.net/11693/28513
dc.descriptionDate of Conference: 23-26 Aug. 2010
dc.descriptionConference name: 20th International Conference on Pattern Recognition, 2010
dc.description.abstractWe present solutions to two problems that prevent the effective use of population-based algorithms in clustering problems. The first solution presents a new representation for arbitrary covariance matrices that allows independent updating of individual parameters while retaining the validity of the matrix. The second solution involves an optimization formulation for finding correspondences between different parameter orderings of candidate solutions. The effectiveness of the proposed solutions are demonstrated on a novel clustering algorithm based on particle swarm optimization for the estimation of Gaussian mixture models. © 2010 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings - 20th International Conference on Pattern Recognition, 2010en_US
dc.relation.isversionofhttps://doi.org/10.1109/ICPR.2010.188en_US
dc.subjectCandidate solutionen_US
dc.subjectClustering problemsen_US
dc.subjectCovariance matricesen_US
dc.subjectGaussian mixture modelen_US
dc.subjectMatrixen_US
dc.subjectOptimization formulationsen_US
dc.subjectPopulation-based algorithmen_US
dc.subjectBlind source separationen_US
dc.subjectClustering algorithmsen_US
dc.subjectCovariance matrixen_US
dc.subjectOptical communicationen_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.subjectPattern recognitionen_US
dc.subjectMaximum likelihood estimationen_US
dc.titleMaximum likelihood estimation of Gaussian mixture models using particle swarm optimizationen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage746en_US
dc.citation.epage749en_US
dc.identifier.doi10.1109/ICPR.2010.188en_US
dc.publisherIEEEen_US


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