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Conference Paper: Cross-domain web image annotation

TitleCross-domain web image annotation
Authors
KeywordsCross-domain
Data manifolds
Data representations
Effective solution
Experimental evidence
Issue Date2009
PublisherIEEE, Computer Society.
Citation
The IEEE International Conference on Data Mining Workshops (ICDMW) 2009, Miami, FL., 6 December 2009. In Proceedings of the IEEE International Conference on Data Mining, 2009, p. 184-189 How to Cite?
AbstractIn recent years, cross-domain learning algorithms have attracted much attention to solve labeled data insufficient problem. However, these cross-domain learning algorithms cannot be applied for subspace learning, which plays a key role in multimedia, e.g., web image annotation. This paper envisions the cross-domain discriminative subspace learning and provides an effective solution to cross-domain subspace learning. In particular, we propose the cross-domain discriminative Hessian Eigenmaps or CDHE for short. CDHE connects the training and the testing samples by minimizing the quadratic distance between the distribution of the training samples and that of the testing samples. Therefore, a common subspace for data representation can be preserved. We basically expect the discriminative information to separate the concepts in the training set can be shared to separate the concepts in the testing set as well and thus we have a chance to address above cross-domain problem duly. The margin maximization is duly adopted in CDHE so the discriminative information for separating different classes can be well preserved. Finally, CDHE encodes the local geometry of each training class in the local tangent space which is locally isometric to the data manifold and thus can locally preserve the intra-class local geometry. Experimental evidence on real world image datasets demonstrates the effectiveness of CDHE for cross-domain web image annotation. © 2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/125684
ISBN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorSi, Sen_HK
dc.contributor.authorTao, Den_HK
dc.contributor.authorChan, KPen_HK
dc.date.accessioned2010-10-31T11:45:55Z-
dc.date.available2010-10-31T11:45:55Z-
dc.date.issued2009en_HK
dc.identifier.citationThe IEEE International Conference on Data Mining Workshops (ICDMW) 2009, Miami, FL., 6 December 2009. In Proceedings of the IEEE International Conference on Data Mining, 2009, p. 184-189en_HK
dc.identifier.isbn978-1-4244-5384-9-
dc.identifier.urihttp://hdl.handle.net/10722/125684-
dc.description.abstractIn recent years, cross-domain learning algorithms have attracted much attention to solve labeled data insufficient problem. However, these cross-domain learning algorithms cannot be applied for subspace learning, which plays a key role in multimedia, e.g., web image annotation. This paper envisions the cross-domain discriminative subspace learning and provides an effective solution to cross-domain subspace learning. In particular, we propose the cross-domain discriminative Hessian Eigenmaps or CDHE for short. CDHE connects the training and the testing samples by minimizing the quadratic distance between the distribution of the training samples and that of the testing samples. Therefore, a common subspace for data representation can be preserved. We basically expect the discriminative information to separate the concepts in the training set can be shared to separate the concepts in the testing set as well and thus we have a chance to address above cross-domain problem duly. The margin maximization is duly adopted in CDHE so the discriminative information for separating different classes can be well preserved. Finally, CDHE encodes the local geometry of each training class in the local tangent space which is locally isometric to the data manifold and thus can locally preserve the intra-class local geometry. Experimental evidence on real world image datasets demonstrates the effectiveness of CDHE for cross-domain web image annotation. © 2009 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE, Computer Society.-
dc.relation.ispartofProceedings of the IEEE International Conference on Data Miningen_HK
dc.rightsIEEE International Conference on Data Mining Proceedings. Copyright © IEEE, Computer Society.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©20xx IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectCross-domain-
dc.subjectData manifolds-
dc.subjectData representations-
dc.subjectEffective solution-
dc.subjectExperimental evidence-
dc.titleCross-domain web image annotationen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4244-5384-9&volume=&spage=184&epage=189&date=2009&atitle=Cross-domain+web+image+annotation-
dc.identifier.emailChan, KP:kpchan@cs.hku.hken_HK
dc.identifier.authorityChan, KP=rp00092en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDMW.2009.47en_HK
dc.identifier.scopuseid_2-s2.0-77951199640en_HK
dc.identifier.hkuros176309en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77951199640&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage184en_HK
dc.identifier.epage189en_HK
dc.identifier.isiWOS:000290247100029-
dc.description.otherThe IEEE International Conference on Data Mining Workshops (ICDMW) 2009, Miami, FL., 6 December 2009. In Proceedings of the IEEE International Conference on Data Mining, 2009, p. 184-189-
dc.identifier.scopusauthoridSi, S=35422764200en_HK
dc.identifier.scopusauthoridTao, D=7102600334en_HK
dc.identifier.scopusauthoridChan, KP=7406032820en_HK
dc.identifier.citeulike8992092-

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