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    Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

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    Type
    Conference Paper
    Authors
    Wang, Jingbin
    Zhou, Yihua
    Duan, Kanghong
    Wang, Jim Jing-Yan
    Bensmail, Halima
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2016-01-15
    Online Publication Date
    2016-01-15
    Print Publication Date
    2015-10
    Permanent link to this record
    http://hdl.handle.net/10754/609037
    
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    Abstract
    In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., An image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods.
    Citation
    Wang, J., Zhou, Y., Duan, K., Wang, J. J.-Y., & Bensmail, H. (2015). Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification. 2015 IEEE International Conference on Systems, Man, and Cybernetics. doi:10.1109/smc.2015.329
    Sponsors
    The research reported in this publication was supported by competitive research funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2015 IEEE International Conference on Systems, Man, and Cybernetics
    Conference/Event name
    Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on
    DOI
    10.1109/SMC.2015.329
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7379461
    ae974a485f413a2113503eed53cd6c53
    10.1109/SMC.2015.329
    Scopus Count
    Collections
    Conference Papers; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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