DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kye-Hyeon Kim | - |
dc.contributor.author | Choi, S | - |
dc.date.accessioned | 2016-03-31T07:29:51Z | - |
dc.date.available | 2016-03-31T07:29:51Z | - |
dc.date.created | 2015-02-17 | - |
dc.date.issued | 2014-08-01 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.other | 2014-OAK-0000032050 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/13696 | - |
dc.description.abstract | Semi-supervised learning (SSL) is attractive for labeling a large amount of data. Motivated from cluster assumption, we present a path-based SSL framework for efficient large-scale SSL, propagating labels through only a few important paths between labeled nodes and unlabeled nodes. From the framework, minimax paths emerge as a minimal set of important paths in a graph, leading us to a novel algorithm, minimax label propagation. With an appropriate stopping criterion, learning time is (1) linear with respect to the number of nodes in a graph and (2) independent of the number of classes. Experimental results show the superiority of our method over existing SSL methods, especially on large-scale data with many classes. (C) 2014 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | Elsevier | - |
dc.relation.isPartOf | PATTERN RECOGNITION LETTERS | - |
dc.subject | Label propagation | - |
dc.subject | Minimax path | - |
dc.subject | Semi-supervised learning | - |
dc.subject | COLLABORATIVE RECOMMENDATION | - |
dc.title | Label propagation through minimax paths for scalable semi-supervised learning | - |
dc.type | Article | - |
dc.contributor.college | 정보전자융합공학부 | - |
dc.identifier.doi | 10.1016/J.PATREC.2014.02.020 | - |
dc.author.google | Kim, KH | - |
dc.author.google | Choi, S | - |
dc.relation.volume | 45 | - |
dc.relation.startpage | 17 | - |
dc.relation.lastpage | 25 | - |
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.45, pp.17 - 25 | - |
dc.identifier.wosid | 000337219200003 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 25 | - |
dc.citation.startPage | 17 | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 45 | - |
dc.contributor.affiliatedAuthor | Choi, S | - |
dc.identifier.scopusid | 2-s2.0-84897560409 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 5 | - |
dc.description.scptc | 5 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Label propagation | - |
dc.subject.keywordAuthor | Minimax path | - |
dc.subject.keywordAuthor | Semi-supervised learning | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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