DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, K | - |
dc.contributor.author | Lee, JM | - |
dc.contributor.author | Lee, IB | - |
dc.date.accessioned | 2016-04-01T02:05:21Z | - |
dc.date.available | 2016-04-01T02:05:21Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2005-10-28 | - |
dc.identifier.issn | 0169-7439 | - |
dc.identifier.other | 2005-OAK-0000005415 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/24389 | - |
dc.description.abstract | This paper introduces a novel multivariate regression approach based on kernel partial least squares (KPLS) with orthogonal signal correction (OSC). OSC has been proposed as a data preprocessing method that removes from X information not correlated to Y. KPLS is a promising regression method for tackling nonlinear systems because it can efficiently compute regression coefficients in high-dimensional feature spaces by means of nonlinear kernel functions. Unlike other nonlinear partial least squares (PLS) techniques KPLS does not entail any nonlinear optimization procedures and has a complexity similar to that of linear PLS. In this paper, the prediction performance of the proposed approach (OSC-KPLS) is compared to those of PLS, OSC-PLS and KPLS using three examples. OSC-KPLS effectively simplifies both the structure and interpretation of the resulting regression model and shows superior prediction performance compared to linear PLS. (c) 2005 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.subject | partial least squares (PLS) | - |
dc.subject | kernel partial least squares (KPLS) | - |
dc.subject | orthogonal signal correction (OSC) | - |
dc.subject | multivariate data analysis | - |
dc.subject | NEAR-INFRARED SPECTRA | - |
dc.subject | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject | REFLECTANCE SPECTRA | - |
dc.subject | NEURAL NETWORKS | - |
dc.subject | PLS | - |
dc.subject | CALIBRATION | - |
dc.subject | MODEL | - |
dc.title | A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction | - |
dc.type | Article | - |
dc.contributor.college | 화학공학과 | - |
dc.identifier.doi | 10.1016/j.chemolab.2005.03.003 | - |
dc.author.google | Kim, K | - |
dc.author.google | Lee, JM | - |
dc.author.google | Lee, IB | - |
dc.relation.volume | 79 | - |
dc.relation.issue | 1-2 | - |
dc.relation.startpage | 22 | - |
dc.relation.lastpage | 30 | - |
dc.contributor.id | 10104673 | - |
dc.relation.journal | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, v.79, no.1-2, pp.22 - 30 | - |
dc.identifier.wosid | 000232000300003 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 30 | - |
dc.citation.number | 1-2 | - |
dc.citation.startPage | 22 | - |
dc.citation.title | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.citation.volume | 79 | - |
dc.contributor.affiliatedAuthor | Lee, IB | - |
dc.identifier.scopusid | 2-s2.0-24044461725 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 123 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | NEAR-INFRARED SPECTRA | - |
dc.subject.keywordPlus | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | REFLECTANCE SPECTRA | - |
dc.subject.keywordPlus | NEURAL NETWORKS | - |
dc.subject.keywordPlus | PLS | - |
dc.subject.keywordPlus | CALIBRATION | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | partial least squares (PLS) | - |
dc.subject.keywordAuthor | kernel partial least squares (KPLS) | - |
dc.subject.keywordAuthor | orthogonal signal correction (OSC) | - |
dc.subject.keywordAuthor | multivariate data analysis | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalResearchArea | Mathematics | - |
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