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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lee, SH | - |
| dc.contributor.author | Choi, S | - |
| dc.date.accessioned | 2016-04-01T01:32:17Z | - |
| dc.date.available | 2016-04-01T01:32:17Z | - |
| dc.date.created | 2009-02-28 | - |
| dc.date.issued | 2007-10 | - |
| dc.identifier.issn | 1070-9908 | - |
| dc.identifier.other | 2007-OAK-0000007196 | - |
| dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/23152 | - |
| dc.description.abstract | In this letter, we present a method of two-dimensional canonical correlation analysis (2D-CCA) where we extend the standard CCA in such a way that relations between two different sets of image data are directly sought without reshaping images into,vectors. We stress that 2D-CCA dramatically reduces the computational complexity, compared to the standard CCA. We show the useful behavior of 2D-CCA through numerical examples of correspondence learning between face images in different poses and illumination conditions. | - |
| dc.description.statementofresponsibility | X | - |
| dc.language | English | - |
| dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGI | - |
| dc.relation.isPartOf | IEEE SIGNAL PROCESSING LETTERS | - |
| dc.subject | canonical correlation analysis (CCA) | - |
| dc.subject | correspondence learning | - |
| dc.subject | two-dimensional analysis | - |
| dc.title | Two-dimensional canonical correlation analysis | - |
| dc.type | Article | - |
| dc.contributor.college | 컴퓨터공학과 | - |
| dc.identifier.doi | 10.1109/LSP.2007.896 | - |
| dc.author.google | Lee, SH | - |
| dc.author.google | Choi, S | - |
| dc.relation.volume | 14 | - |
| dc.relation.issue | 10 | - |
| dc.relation.startpage | 735 | - |
| dc.relation.lastpage | 738 | - |
| dc.contributor.id | 10077620 | - |
| dc.relation.journal | IEEE SIGNAL PROCESSING LETTERS | - |
| dc.relation.index | SCI급, SCOPUS 등재논문 | - |
| dc.relation.sci | SCIE | - |
| dc.collections.name | Journal Papers | - |
| dc.type.rims | ART | - |
| dc.identifier.bibliographicCitation | IEEE SIGNAL PROCESSING LETTERS, v.14, no.10, pp.735 - 738 | - |
| dc.identifier.wosid | 000249941900023 | - |
| dc.date.tcdate | 2019-01-01 | - |
| dc.citation.endPage | 738 | - |
| dc.citation.number | 10 | - |
| dc.citation.startPage | 735 | - |
| dc.citation.title | IEEE SIGNAL PROCESSING LETTERS | - |
| dc.citation.volume | 14 | - |
| dc.contributor.affiliatedAuthor | Choi, S | - |
| dc.identifier.scopusid | 2-s2.0-34548808579 | - |
| dc.description.journalClass | 1 | - |
| dc.description.journalClass | 1 | - |
| dc.description.wostc | 32 | - |
| dc.type.docType | Article | - |
| dc.subject.keywordAuthor | canonical correlation analysis (CCA) | - |
| dc.subject.keywordAuthor | correspondence learning | - |
| dc.subject.keywordAuthor | two-dimensional analysis | - |
| dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Engineering | - |
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Related Researcher
- 최승진CHOI, SEUNGJIN
- Dept of Computer Science & Enginrg