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Almost autonomous training of mixtures of principal component analyzers

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dc.contributor.author Musa, Mohamed E. M.
dc.contributor.author Ridder, Dick de
dc.contributor.author Duin, Robert P. W.
dc.contributor.author Atalay, Volkan
dc.date.accessioned 2020-04-18T13:27:21Z
dc.date.available 2020-04-18T13:27:21Z
dc.date.issued 2004-07-02
dc.identifier.citation Musa, MEM; de Ridder, D.; Duin, RPW; Atalay, V., "Almost autonomous training of mixtures of principal component analyzers" Pattern Recognition Letters, Vol.25, No.9, pp.1085-1095, (2004). tr_TR
dc.identifier.issn 0167-8655
dc.identifier.uri http://hdl.handle.net/20.500.12416/3334
dc.description.abstract In recent years, a number of mixtures of local PCA models have been proposed. Most of these models require the user to set the number of submodels (local models) in the mixture and the dimensionality of the submodels (i.e., number of PC's) as well. To make the model free of these parameters, we propose a greedy expectation-maximization algorithm to find a suboptimal number of submodels. For a given retained variance ratio, the proposed algorithm estimates for each submodel the dimensionality that retains this given variability ratio. We test the proposed method on two different classification problems: handwritten digit recognition and 2-class ionosphere data classification. The results show that the proposed method has a good performance. tr_TR
dc.language.iso eng tr_TR
dc.publisher Elsevier Science BV tr_TR
dc.relation.isversionof 10.1016/j.patrec.2004.03.019 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject PCA Mixture Model tr_TR
dc.subject EM Algorithm tr_TR
dc.subject Regularization tr_TR
dc.title Almost autonomous training of mixtures of principal component analyzers tr_TR
dc.type article tr_TR
dc.relation.journal Pattern Recognition Letters tr_TR
dc.identifier.volume 25 tr_TR
dc.identifier.issue 9 tr_TR
dc.identifier.startpage 1085 tr_TR
dc.identifier.endpage 1095 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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