Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118109
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLee, S.X.en
dc.contributor.authorLeemaqz, K.L.en
dc.contributor.authorMcLachlan, G.J.en
dc.date.issued2018en
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2018; 29(11):5581-5591en
dc.identifier.issn2162-237Xen
dc.identifier.issn2162-2388en
dc.identifier.urihttp://hdl.handle.net/2440/118109-
dc.description.abstractFinite mixtures of skew distributions provide a flexible tool for modeling heterogeneous data with asymmetric distributional features. However, parameter estimation via the Expectation-Maximization (EM) algorithm can become very time consuming due to the complicated expressions involved in the E-step that are numerically expensive to evaluate. While parallelizing the EM algorithm can offer considerable speedup in time performance, current implementations focus almost exclusively on distributed platforms. In this paper, we consider instead the most typical operating environment for users of mixture models-a standalone multicore machine and the R programming environment. We develop a block implementation of the EM algorithm that facilitates the calculations on the E- and M-steps to be spread across a number of threads. We focus on the fitting of finite mixtures of multivariate skew normal and skew distributions, and show that both the E- and M-steps in the EM algorithm can be modified to allow the data to be split into blocks. Our approach is easy to implement and provides immediate benefits to users of multicore machines. Experiments were conducted on two real data sets to demonstrate the effectiveness of the proposed approach.en
dc.description.statementofresponsibilitySharon X. Lee, Kaleb L. Leemaqz and Geoffrey J. McLachlanen
dc.language.isoenen
dc.publisherIEEEen
dc.rights© 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.en
dc.subjectExpectation–Maximization (EM) algorithm; mixture models; parallel algorithm; skew distributionsen
dc.titleA block EM algorithm for multivariate skew normal and skew t-mixture modelsen
dc.typeJournal articleen
dc.identifier.rmid0030107864en
dc.identifier.doi10.1109/TNNLS.2018.2805317en
dc.identifier.pubid458220-
pubs.library.collectionMedicine publicationsen
pubs.library.teamDS10en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidLeemaqz, K.L. [0000-0002-6391-135X]en
Appears in Collections:Medicine publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.