Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/207229
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dc.contributor.authorMin Jiangen_US
dc.contributor.authorWenzhen Huangen_US
dc.contributor.authorZhongqiang Huangen_US
dc.contributor.authorGary G. Yenen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:56:13Z-
dc.date.available2020-04-06T07:56:13Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TCYB.2015.2502483en_US
dc.identifier.urihttp://localhost/handle/Hannan/207229-
dc.description.abstractDomain adaptation learning (DAL) investigates how to perform a task across different domains. In this paper, we present a kernelized local-global approach to solve domain adaptation problems. The basic idea of the proposed method is to consider the global and local information regarding the domains (e.g., maximum mean discrepancy and intraclass distance) and to convert the domain adaptation problem into a bi-object optimization problem via the kernel method. A solution for the optimization problem will help us identify a latent space in which the distributions of the different domains will be close to each other in the global sense, and the local properties of the labeled source samples will be preserved. Therefore, classic classification algorithms can be used to recognize unlabeled target domain data, which has a significant difference on the source samples. Based on the analysis, we validate the proposed algorithm using four different sources of data: synthetic, textual, object, and facial image. The experimental results indicate that the proposed method provides a reasonable means to improve DAL algorithms.en_US
dc.format.extent38,en_US
dc.format.extent51en_US
dc.publisherIEEEen_US
dc.relation.haspart7349204.pdfen_US
dc.titleIntegration of Global and Local Metrics for Domain Adaptation Learning Via Dimensionality Reductionen_US
dc.typeArticleen_US
dc.journal.volume47en_US
dc.journal.issue1en_US
Appears in Collections:2017

Files in This Item:
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7349204.pdf2.67 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMin Jiangen_US
dc.contributor.authorWenzhen Huangen_US
dc.contributor.authorZhongqiang Huangen_US
dc.contributor.authorGary G. Yenen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:56:13Z-
dc.date.available2020-04-06T07:56:13Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TCYB.2015.2502483en_US
dc.identifier.urihttp://localhost/handle/Hannan/207229-
dc.description.abstractDomain adaptation learning (DAL) investigates how to perform a task across different domains. In this paper, we present a kernelized local-global approach to solve domain adaptation problems. The basic idea of the proposed method is to consider the global and local information regarding the domains (e.g., maximum mean discrepancy and intraclass distance) and to convert the domain adaptation problem into a bi-object optimization problem via the kernel method. A solution for the optimization problem will help us identify a latent space in which the distributions of the different domains will be close to each other in the global sense, and the local properties of the labeled source samples will be preserved. Therefore, classic classification algorithms can be used to recognize unlabeled target domain data, which has a significant difference on the source samples. Based on the analysis, we validate the proposed algorithm using four different sources of data: synthetic, textual, object, and facial image. The experimental results indicate that the proposed method provides a reasonable means to improve DAL algorithms.en_US
dc.format.extent38,en_US
dc.format.extent51en_US
dc.publisherIEEEen_US
dc.relation.haspart7349204.pdfen_US
dc.titleIntegration of Global and Local Metrics for Domain Adaptation Learning Via Dimensionality Reductionen_US
dc.typeArticleen_US
dc.journal.volume47en_US
dc.journal.issue1en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7349204.pdf2.67 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMin Jiangen_US
dc.contributor.authorWenzhen Huangen_US
dc.contributor.authorZhongqiang Huangen_US
dc.contributor.authorGary G. Yenen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:56:13Z-
dc.date.available2020-04-06T07:56:13Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TCYB.2015.2502483en_US
dc.identifier.urihttp://localhost/handle/Hannan/207229-
dc.description.abstractDomain adaptation learning (DAL) investigates how to perform a task across different domains. In this paper, we present a kernelized local-global approach to solve domain adaptation problems. The basic idea of the proposed method is to consider the global and local information regarding the domains (e.g., maximum mean discrepancy and intraclass distance) and to convert the domain adaptation problem into a bi-object optimization problem via the kernel method. A solution for the optimization problem will help us identify a latent space in which the distributions of the different domains will be close to each other in the global sense, and the local properties of the labeled source samples will be preserved. Therefore, classic classification algorithms can be used to recognize unlabeled target domain data, which has a significant difference on the source samples. Based on the analysis, we validate the proposed algorithm using four different sources of data: synthetic, textual, object, and facial image. The experimental results indicate that the proposed method provides a reasonable means to improve DAL algorithms.en_US
dc.format.extent38,en_US
dc.format.extent51en_US
dc.publisherIEEEen_US
dc.relation.haspart7349204.pdfen_US
dc.titleIntegration of Global and Local Metrics for Domain Adaptation Learning Via Dimensionality Reductionen_US
dc.typeArticleen_US
dc.journal.volume47en_US
dc.journal.issue1en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7349204.pdf2.67 MBAdobe PDF