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    A matrix factorization framework for jointly analyzing multiple nonnegative data

    173059_50447_Gupta TextMiningWorkshop_SDM_11.pdf (183.7Kb)
    Access Status
    Open access
    Authors
    Gupta, Sunil
    Phung, Dinh
    Adams, Brett
    Venkatesh, Svetha
    Date
    2011
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Gupta, 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
    Source Title
    Proceedings of the Ninth Workshop on Text Mining - Eleventh SIAM International Conference on Data Mining
    Source Conference
    Text Mining 2011 - SDM11
    ISBN
    9780898719925
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/16617
    Collection
    • Curtin Research Publications
    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.

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