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    A bayesian framework for learning shared and individual subspaces from multiple data sources

    Access Status
    Fulltext not available
    Authors
    Gupta, Sunil
    Phung, Dinh
    Adams, Brett
    Venkatesh, Svetha
    Date
    2011
    Type
    Conference Paper
    
    Metadata
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    Citation
    Gupta, Sunil Kumar and Phung, Dinh and Adams, Brett and Venkatesh, Svetha. 2011. A bayesian framework for learning shared and individual subspaces from multiple data sources, in Huang, J. Z. and Cao, L. and Srivastava J. (ed), Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD, May 24-27 2011, pp. 136-147. Shenzhen, China: Springer.
    Source Title
    Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference
    Source Conference
    PAKDD 2011
    DOI
    10.1007/978-3-642-20841-6_12
    ISBN
    9783642208409
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/32333
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a novel Bayesian formulation to exploit shared structures across multiple data sources, constructing foundations for effective mining and retrieval across disparate domains. We jointly analyze diverse data sources using a unifying piece of metadata (textual tags). We propose a method based on Bayesian Probabilistic Matrix Factorization (BPMF) which is able to explicitly model the partial knowledge common to the datasets using shared subspaces and the knowledge specific to each dataset using individual subspaces. For the proposed model, we derive an efficient algorithm for learning the joint factorization based on Gibbs sampling. The effectiveness of the model is demonstrated by social media retrieval tasks across single and multiple media. The proposed solution is applicable to a wider context, providing a formal framework suitable for exploiting individual as well as mutual knowledge present across heterogeneous data sources of many kinds.

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