Real-time sufficient dimension reduction through principal least squares support vector machines
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Artemiou, Andreas | - |
dc.contributor.author | Dong, Yuexiao | - |
dc.contributor.author | Shin, Seung Jun | - |
dc.date.accessioned | 2021-08-30T02:48:24Z | - |
dc.date.available | 2021-08-30T02:48:24Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/49459 | - |
dc.description.abstract | We propose a real-time approach for sufficient dimension reduction. Compared with popular sufficient dimension reduction methods including sliced inverse regression and principal support vector machines, the proposed principal least squares support vector machines approach enjoys better estimation of the central subspace. Furthermore, this new proposal can be used in the presence of streamed data for quick real-time updates. It is demonstrated through simulations and real data applications that our proposal performs better and faster than existing algorithms in the literature. (c) 2020 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Real-time sufficient dimension reduction through principal least squares support vector machines | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shin, Seung Jun | - |
dc.identifier.doi | 10.1016/j.patcog.2020.107768 | - |
dc.identifier.scopusid | 2-s2.0-85097898629 | - |
dc.identifier.wosid | 000615938800013 | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION, v.112 | - |
dc.relation.isPartOf | PATTERN RECOGNITION | - |
dc.citation.title | PATTERN RECOGNITION | - |
dc.citation.volume | 112 | - |
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.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | SLICED INVERSE REGRESSION | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | SUBSPACE | - |
dc.subject.keywordAuthor | Central subspace | - |
dc.subject.keywordAuthor | Ladle estimator | - |
dc.subject.keywordAuthor | Online sliced inverse regression | - |
dc.subject.keywordAuthor | Principal support vector machines | - |
dc.subject.keywordAuthor | Streamed data | - |
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