DC Field | Value | Language |
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dc.contributor.author | Choi, H | - |
dc.contributor.author | Choi, S | - |
dc.date.accessioned | 2016-04-01T01:46:35Z | - |
dc.date.available | 2016-04-01T01:46:35Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2007-03 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.other | 2006-OAK-0000006439 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/23677 | - |
dc.description.abstract | Isomap is one of widely used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional scaling). In this paper we pay our attention to two critical issues that were not considered in Isomap, such as: (1) generalization property (projection property); (2) topological stability. Then we present a robust kernel Isomap method, armed with such two properties. We present a method which relates the Isomap to Mercer kernel machines, so that the generalization property naturally emerges, through kernel principal component analysis. For topological stability, we investigate the network flow in a graph, providing a method for eliminating critical outliers. The useful behavior of the robust kernel Isomap is confirmed through numerical experiments with several data sets. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | PATTERN RECOGNITION | - |
dc.subject | isomap | - |
dc.subject | kernel PCA | - |
dc.subject | manifold learning | - |
dc.subject | multidimensional scaling (MDS) | - |
dc.subject | nonlinear dimensionality reduction | - |
dc.subject | NONLINEAR DIMENSIONALITY REDUCTION | - |
dc.title | Robust kernel Isomap | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1016/j.patcog.2006.04.025 | - |
dc.author.google | Choi, H | - |
dc.author.google | Choi, S | - |
dc.relation.volume | 40 | - |
dc.relation.issue | 3 | - |
dc.relation.startpage | 853 | - |
dc.relation.lastpage | 862 | - |
dc.contributor.id | 10077620 | - |
dc.relation.journal | PATTERN RECOGNITION | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION, v.40, no.3, pp.853 - 862 | - |
dc.identifier.wosid | 000242684100007 | - |
dc.date.tcdate | 2018-12-01 | - |
dc.citation.endPage | 862 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 853 | - |
dc.citation.title | PATTERN RECOGNITION | - |
dc.citation.volume | 40 | - |
dc.contributor.affiliatedAuthor | Choi, S | - |
dc.identifier.scopusid | 2-s2.0-33750499496 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 65 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | isomap | - |
dc.subject.keywordAuthor | kernel PCA | - |
dc.subject.keywordAuthor | manifold learning | - |
dc.subject.keywordAuthor | multidimensional scaling (MDS) | - |
dc.subject.keywordAuthor | nonlinear dimensionality reduction | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
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