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Conference Paper: Learning discriminative correlation subspace for heterogeneous domain adaptation

TitleLearning discriminative correlation subspace for heterogeneous domain adaptation
Authors
Issue Date2017
Citation
26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, 19-25 August 2017. In Conference Proceedings, 2017, p. 3252-3258 How to Cite?
AbstractDomain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data, and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.
Persistent Identifierhttp://hdl.handle.net/10722/276556
ISSN
2020 SCImago Journal Rankings: 0.649

 

DC FieldValueLanguage
dc.contributor.authorYan, Yuguang-
dc.contributor.authorLi, Wen-
dc.contributor.authorNg, Michael-
dc.contributor.authorTan, Mingkui-
dc.contributor.authorWu, Hanrui-
dc.contributor.authorMin, Huaqing-
dc.contributor.authorWu, Qingyao-
dc.date.accessioned2019-09-18T08:33:58Z-
dc.date.available2019-09-18T08:33:58Z-
dc.date.issued2017-
dc.identifier.citation26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, 19-25 August 2017. In Conference Proceedings, 2017, p. 3252-3258-
dc.identifier.issn1045-0823-
dc.identifier.urihttp://hdl.handle.net/10722/276556-
dc.description.abstractDomain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data, and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.-
dc.languageeng-
dc.relation.ispartofInternational Joint Conference on Artificial Intelligence (IJCAI)-
dc.titleLearning discriminative correlation subspace for heterogeneous domain adaptation-
dc.typeConference_Paper-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.24963/ijcai.2017/454-
dc.identifier.scopuseid_2-s2.0-85031892773-
dc.identifier.spage3252-
dc.identifier.epage3258-
dc.identifier.issnl1045-0823-

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