DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, YJ | - |
dc.contributor.author | Choi, SJ | - |
dc.date.accessioned | 2016-04-01T02:09:01Z | - |
dc.date.available | 2016-04-01T02:09:01Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2005-07-15 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.other | 2005-OAK-0000005205 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/24530 | - |
dc.description.abstract | This paper addresses a new method and aspect of information-theoretic Clustering where we exploit the minimum entropy principle and the quadratic distance measure between probability densities, We present a new minimum entropy objective function which leads to the maximization or within-cluster association, A simple implementation using the gradient ascent method is given. In addition, we show that the Minimum entropy principle leads to the objective function of the k-means clustering, and the maximum within-cluster association is closed related to the spectral clustering which is an eigen-decomposition-based method. This information-theoretic view of spectral clustering leads us to use the kernel density estimation method in constructing an affinity matrix. (c) 2004 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | PATTERN RECOGNITION LETTERS | - |
dc.subject | clustering | - |
dc.subject | information-theoretic learning | - |
dc.subject | minimum entropy | - |
dc.subject | spectral clustering | - |
dc.title | Maximum within-cluster association | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1016/j.patrec.2004.11.025 | - |
dc.author.google | Lee, YJ | - |
dc.author.google | Choi, SJ | - |
dc.relation.volume | 26 | - |
dc.relation.issue | 10 | - |
dc.relation.startpage | 1412 | - |
dc.relation.lastpage | 1422 | - |
dc.contributor.id | 10077620 | - |
dc.relation.journal | PATTERN RECOGNITION LETTERS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION LETTERS, v.26, no.10, pp.1412 - 1422 | - |
dc.identifier.wosid | 000230006800002 | - |
dc.date.tcdate | 2019-02-01 | - |
dc.citation.endPage | 1422 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 1412 | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 26 | - |
dc.contributor.affiliatedAuthor | Choi, SJ | - |
dc.identifier.scopusid | 2-s2.0-19744367328 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 2 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | information-theoretic learning | - |
dc.subject.keywordAuthor | minimum entropy | - |
dc.subject.keywordAuthor | spectral clustering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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