Model selection for mixture model via integrated nested Laplace approximation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Ji Won | - |
dc.date.accessioned | 2021-09-04T18:05:32Z | - |
dc.date.available | 2021-09-04T18:05:32Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2015-03-19 | - |
dc.identifier.issn | 0013-5194 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/94105 | - |
dc.description.abstract | To cluster or partition data/signal, expectation-and-maximisation or variational approximation with a mixture model (MM), which is a parametric probability density function represented as a weighted sum of (K) over cap densities, is often used. However, model selection to find the underlying (K) over cap is one of the key concerns in MM clustering, since the desired clusters can be obtained only when (K) over cap is known. A new model selection algorithm to explore (K) over cap in a Bayesian framework is proposed. The proposed algorithm builds the density of the model order which information criterion such as AIC and BIC or other heuristic algorithms basically fail to reconstruct. In addition, this algorithm reconstructs the density quickly as compared with the time-consuming Monte Carlo simulation using integrated nested Laplace approximation. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | INST ENGINEERING TECHNOLOGY-IET | - |
dc.subject | NUMBER | - |
dc.subject | DISTRIBUTIONS | - |
dc.subject | COMPONENTS | - |
dc.subject | CLUSTERS | - |
dc.title | Model selection for mixture model via integrated nested Laplace approximation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Ji Won | - |
dc.identifier.doi | 10.1049/el.2014.4338 | - |
dc.identifier.scopusid | 2-s2.0-84924729946 | - |
dc.identifier.wosid | 000351271700028 | - |
dc.identifier.bibliographicCitation | ELECTRONICS LETTERS, v.51, no.6, pp.484 - 485 | - |
dc.relation.isPartOf | ELECTRONICS LETTERS | - |
dc.citation.title | ELECTRONICS LETTERS | - |
dc.citation.volume | 51 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 484 | - |
dc.citation.endPage | 485 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | NUMBER | - |
dc.subject.keywordPlus | DISTRIBUTIONS | - |
dc.subject.keywordPlus | COMPONENTS | - |
dc.subject.keywordPlus | CLUSTERS | - |
dc.subject.keywordAuthor | video coding | - |
dc.subject.keywordAuthor | image resolution | - |
dc.subject.keywordAuthor | image reconstruction | - |
dc.subject.keywordAuthor | extraction time | - |
dc.subject.keywordAuthor | computational complexity | - |
dc.subject.keywordAuthor | 4x4 boundary pixels | - |
dc.subject.keywordAuthor | high-efficiency video coding | - |
dc.subject.keywordAuthor | intra-prediction mode | - |
dc.subject.keywordAuthor | fast thumbnail extraction method | - |
dc.subject.keywordAuthor | HEVC | - |
dc.subject.keywordAuthor | prediction modes | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.