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dc.contributor.authorGupta, Sunil
dc.contributor.authorPhung, Dinh
dc.contributor.authorAdams, Brett
dc.contributor.authorTran, Truyen
dc.contributor.authorVenkatesh, Svetha
dc.contributor.editorA. Tompkins, Q. Yang, R. Bharat Rao & B. Krishnapuram
dc.date.accessioned2017-01-30T12:04:35Z
dc.date.available2017-01-30T12:04:35Z
dc.date.created2011-02-20T20:01:12Z
dc.date.issued2010
dc.identifier.citationGupta, Sunil Kumar and Phung, Dinh and Adams, Brett and Tran, Truyen and Venkatesh, Svetha. 2010. Nonnegative shared subspace learning and its application to social media retrieval, in Tompkins, A. and Yang, Q. and Bharat R. and Krishnapuram, B. (ed), 16th ACM SIGKDD international conference on Knowledge discovery and data mining, Jul 24 2010, pp. 1169-1178. Washington DC: Association for Computing Machinery.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/17861
dc.identifier.doi10.1145/1835804.1835951
dc.description.abstract

Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can seriously affect the precision of a social tag-based web retrieval system. Therefore improving the precision performance of these social tag-based web retrieval systems has become an increasingly important research topic. To this end, we propose a shared subspace learning framework to leverage a secondary source to improve retrieval performance from a primary dataset.This is achieved by learning a shared subspace between the two sources under a joint Nonnegative Matrix Factorization in which the level of subspace sharing can be explicitly controlled. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. We validate the framework on image and video retrieval tasks in which tags from the LabelMe dataset are used to improve image retrieval performance from a Flickr dataset and video retrieval performance from a YouTube dataset. This has implications for how to exploit and transfer knowledge from readily available auxiliary tagging resources to improve another social web retrieval system. Our shared subspace learning framework is applicable to a range of problems where one needs to exploit the strengths existing among multiple and heterogeneous datasets.

dc.publisherACM
dc.subjectsocial media
dc.subjectnonnegative shared subspace learning
dc.subjecttransfer learning
dc.subjectimage and video retrieval
dc.titleNonnegative shared subspace learning and its application to social media retrieval
dc.typeConference Paper
dcterms.source.titleThe 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
dcterms.source.seriesThe 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
dcterms.source.isbn9781450300551
dcterms.source.conferenceKDD 2010
dcterms.source.conference-start-dateJul 24 2010
dcterms.source.conferencelocationWashingtone DC
dcterms.source.placeUSA
curtin.departmentDepartment of Computing
curtin.accessStatusOpen access via publisher


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