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Article: Distribution calibration in Riemannian symmetric space

TitleDistribution calibration in Riemannian symmetric space
Authors
KeywordsCross-domain learning
distribution calibration
Riemannian symmetric space
subspace learning
Issue Date2011
PublisherIEEE.
Citation
IEEE Transactions On Systems, Man, And Cybernetics, Part B: Cybernetics, 2011, v. 41 n. 4, p. 921-930 How to Cite?
AbstractDistribution calibration plays an important role in cross-domain learning. However, existing distribution distance metrics are not geodesic; therefore, they cannot measure the intrinsic distance between two distributions. In this paper, we calibrate two distributions by using the geodesic distance in Riemannian symmetric space. Our method learns a latent subspace in the reproducing kernel Hilbert space, where the geodesic distance between the distribution of the source and the target domains is minimized. The corresponding geodesic distance is thus equivalent to the geodesic distance between two symmetric positive definite (SPD) matrices defined in the Riemannian symmetric space. These two SPD matrices parameterize the marginal distributions of the source and target domains in the latent subspace. We carefully design an evolutionary algorithm to find a local optimal solution that minimizes this geodesic distance. Empirical studies on face recognition, text categorization, and web image annotation suggest the effectiveness of the proposed scheme. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/140799
ISSN
2014 Impact Factor: 6.22
2015 SCImago Journal Rankings: 3.921
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorSi, Sen_HK
dc.contributor.authorLiu, Wen_HK
dc.contributor.authorTao, Den_HK
dc.contributor.authorChan, KPen_HK
dc.date.accessioned2011-09-23T06:19:30Z-
dc.date.available2011-09-23T06:19:30Z-
dc.date.issued2011en_HK
dc.identifier.citationIEEE Transactions On Systems, Man, And Cybernetics, Part B: Cybernetics, 2011, v. 41 n. 4, p. 921-930en_HK
dc.identifier.issn1083-4419en_HK
dc.identifier.urihttp://hdl.handle.net/10722/140799-
dc.description.abstractDistribution calibration plays an important role in cross-domain learning. However, existing distribution distance metrics are not geodesic; therefore, they cannot measure the intrinsic distance between two distributions. In this paper, we calibrate two distributions by using the geodesic distance in Riemannian symmetric space. Our method learns a latent subspace in the reproducing kernel Hilbert space, where the geodesic distance between the distribution of the source and the target domains is minimized. The corresponding geodesic distance is thus equivalent to the geodesic distance between two symmetric positive definite (SPD) matrices defined in the Riemannian symmetric space. These two SPD matrices parameterize the marginal distributions of the source and target domains in the latent subspace. We carefully design an evolutionary algorithm to find a local optimal solution that minimizes this geodesic distance. Empirical studies on face recognition, text categorization, and web image annotation suggest the effectiveness of the proposed scheme. © 2011 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE.en_US
dc.relation.ispartofIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cyberneticsen_HK
dc.rights©2011 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectCross-domain learningen_HK
dc.subjectdistribution calibrationen_HK
dc.subjectRiemannian symmetric spaceen_HK
dc.subjectsubspace learningen_HK
dc.titleDistribution calibration in Riemannian symmetric spaceen_HK
dc.typeArticleen_HK
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/TSMCB.2010.2100042en_HK
dc.identifier.scopuseid_2-s2.0-79960697913en_HK
dc.identifier.hkuros193288en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79960697913&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume41en_HK
dc.identifier.issue4en_HK
dc.identifier.spage921en_HK
dc.identifier.epage930en_HK
dc.identifier.isiWOS:000293708200003-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridSi, S=35422764200en_HK
dc.identifier.scopusauthoridLiu, W=36077704800en_HK
dc.identifier.scopusauthoridTao, D=7102600334en_HK
dc.identifier.scopusauthoridChan, KP=7406032820en_HK

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