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    Nonnegative shared subspace learning and its application to social media retrieval

    Access Status
    Open access via publisher
    Authors
    Gupta, Sunil
    Phung, Dinh
    Adams, Brett
    Tran, Truyen
    Venkatesh, Svetha
    Date
    2010
    Type
    Conference Paper
    
    Metadata
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    Citation
    Gupta, 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.
    Source Title
    The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    Source Conference
    KDD 2010
    DOI
    10.1145/1835804.1835951
    ISBN
    9781450300551
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/17861
    Collection
    • Curtin Research Publications
    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.

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