Bayesian interpretation to generalize adaptive mean shift algorithm
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Ji Won | - |
dc.date.accessioned | 2021-09-04T05:06:25Z | - |
dc.date.available | 2021-09-04T05:06:25Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 1064-1246 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/90192 | - |
dc.description.abstract | The Adaptive Mean Shift (AMS) algorithm is a popular and simple non-parametric clustering approach based on Kernel Density Estimation. In this paper the AMS is reformulated in a Bayesian framework, which permits a natural generalization in several directions and is shown to improve performance. The Bayesian framework considers the AMS to be a method of obtaining a posterior mode. This allows the algorithm to be generalized with three components which are not considered in the conventional approach: node weights, a prior for a particular location, and a posterior distribution for the bandwidth. Practical methods of building the three different components are considered. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IOS PRESS | - |
dc.title | Bayesian interpretation to generalize adaptive mean shift algorithm | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Ji Won | - |
dc.identifier.doi | 10.3233/IFS-162103 | - |
dc.identifier.scopusid | 2-s2.0-84971370549 | - |
dc.identifier.wosid | 000375954300046 | - |
dc.identifier.bibliographicCitation | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, v.30, no.6, pp.3583 - 3592 | - |
dc.relation.isPartOf | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | - |
dc.citation.title | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | - |
dc.citation.volume | 30 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 3583 | - |
dc.citation.endPage | 3592 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordAuthor | Adaptive mean shift algorithm | - |
dc.subject.keywordAuthor | kernel density estimation | - |
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