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dc.contributorThe Pennsylvania State University CiteSeerX Archives
dc.contributor.authorLei Wu
dc.contributor.authorSteven C. H. Hoi
dc.contributor.authorRong Jin
dc.contributor.authorJianke Zhu
dc.contributor.authorNenghai Yu
dc.date.accessioned2019-10-24T03:44:35Z
dc.date.available2019-10-24T03:44:35Z
dc.date.created2017-01-05 00:47
dc.date.issued2016-09-04
dc.identifieroai:CiteSeerX.psu:10.1.1.843.4734
dc.identifierhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.843.4734
dc.identifier.urihttp://hdl.handle.net/20.500.12424/818595
dc.description.abstractLearning distance functions with side information plays a key role in many data mining applications. Conventional distance metric learning approaches often assume the target distance function is represented in some form of Mahalanobis distance. These approaches usually work well when data is in low dimensionality, but often become computationally expensive or even infeasible when handling high-dimensional data. In this paper, we propose a novel scheme of learning nonlinear distance functions with side information. It aims to learn a Bregman distance function using a non-parametric approach that is similar to Support Vector Machines. We emphasize that the proposed scheme is more general than the conventional approach for distance metric learning, and is able to handle high-dimensional data efficiently. We verify the efficacy of the proposed distance learning method with extensive experiments on semi-supervised clustering. The comparison with state-of-the-art approaches for learning distance functions with side information reveals clear advantages of the proposed technique. Index Terms Bregman distance, distance functions, metric learning, convex functions
dc.languageen
dc.language.isoeng
dc.rightsMetadata may be used without restrictions as long as the oai identifier remains attached to it.
dc.titleLearning Bregman Distance Functions for Semi-Supervised Clustering
dc.typetext
ge.collectioncodeOAIDATA
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:10414367
ge.identifier.permalinkhttps://www.globethics.net/gel/10414367
ge.lastmodificationdate2017-01-05 00:47
ge.lastmodificationuseradmin@pointsoftware.ch (import)
ge.submissions0
ge.oai.exportid148934
ge.oai.repositoryid54
ge.oai.streamid2
ge.setnameGlobeEthicsLib
ge.setspecglobeethicslib
ge.linkhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.843.4734
ge.linkhttp://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article%3D3282%26context%3Dsis_research


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