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dc.contributor.authorGupta, Sunil
dc.contributor.authorPhung, Dinh
dc.contributor.authorAdams, Brett
dc.contributor.authorVenkatesh, Svetha
dc.contributor.editorMichael W. Berry and Jacob Kogan
dc.date.accessioned2017-01-30T11:56:47Z
dc.date.available2017-01-30T11:56:47Z
dc.date.created2012-03-01T20:00:56Z
dc.date.issued2011
dc.identifier.citationGupta, Sunil Kumar and Phung, Dinh and Adams, Brett and Venkatesh, Svetha. 2011. A matrix factorization framework for jointly analyzing multiple nonnegative data, in Berry, Michael W. and Kogan, Jacob (ed), Proceedings of the Ninth Workshop on Text Mining - Eleventh SIAM International Conference on Data Mining (SDM11), Apr 30 2011. Mesa, Arizona: Omnipress
dc.identifier.urihttp://hdl.handle.net/20.500.11937/16617
dc.description.abstract

Nonnegative matrix factorization based methods provide one of the simplest and most effective approaches to text mining. However, their applicability is mainly limited to analyzing a single data source. In this paper, we propose a novel joint matrix factorization framework which can jointly analyze multiple data sources by exploiting their shared and individual structures. The proposed framework is flexible to handle any arbitrary sharing configurations encountered in real world data. We derive an efficient algorithm for learning the factorization and show that its convergence is theoretically guaranteed. We demonstrate the utility and effectiveness of the proposed framework in two real-world applications–improving social media retrieval using auxiliary sources and cross-social media retrieval. Representing each social media source using their textual tags, for both applications, we show that retrieval performance exceeds the existing state-of-the-art techniques. The proposed solution provides a generic framework and can be applicable to a wider context in data mining wherever one needs to exploit mutual and individual knowledge present across multiple data sources.

dc.publisherOmnipress
dc.titleA matrix factorization framework for jointly analyzing multiple nonnegative data
dc.typeConference Paper
dcterms.source.titleProceedings of the Ninth Workshop on Text Mining - Eleventh SIAM International Conference on Data Mining
dcterms.source.seriesProceedings of the Ninth Workshop on Text Mining - Eleventh SIAM Int. Conf. on Data Mining
dcterms.source.isbn9780898719925
dcterms.source.conferenceText Mining 2011 - SDM11
dcterms.source.conference-start-dateApr 30 2011
dcterms.source.conferencelocationMesa, Arizona
dcterms.source.placeUSA
curtin.departmentDepartment of Computing
curtin.accessStatusOpen access


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