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    Individuality- and Commonality-Based Multiview Multilabel Learning

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    ICM2L.pdf
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    Type
    Article
    Authors
    Tan, Qiaoyu
    Yu, Guoxian cc
    Wang, Jun cc
    Domeniconi, Carlotta
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-11-19
    Online Publication Date
    2019-11-19
    Print Publication Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/660405
    
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    Abstract
    In multiview multilabel learning, each object is represented by several heterogeneous feature representations and is also annotated with a set of discrete nonexclusive labels. Previous studies typically focus on capturing the shared latent patterns among multiple views, while not sufficiently considering the diverse characteristics of individual views, which can cause performance degradation. In this article, we propose a novel approach [individuality- and commonality-based multiview multilabel learning (ICM2L)] to explicitly explore the individuality and commonality information of multilabel multiple view data in a unified model. Specifically, a common subspace is learned across different views to capture the shared patterns. Then, multiple individual classifiers are exploited to explore the characteristics of individual views. Next, an ensemble strategy is adopted to make a prediction. Finally, we develop an alternative solution to joinly optimize our model, which can enhance the robustness of the proposed model toward rare labels and reinforce the reciprocal effects of individuality and commonality among heterogeneous views, and thus further improve the performance. Experiments on various real-word datasets validate the effectiveness of ICM2L against the state-of-the-art solutions, and ICM2L can leverage the individuality and commonality information to achieve an improved performance as well as to enhance the robustness toward rare labels
    Citation
    Tan, Q., Yu, G., Wang, J., Domeniconi, C., & Zhang, X. (2019). Individuality- and Commonality-Based Multiview Multilabel Learning. IEEE Transactions on Cybernetics, 1–12. doi:10.1109/tcyb.2019.2950560
    Sponsors
    The authors would like to thank the authors who kindly shared their source code and datasets with them for the experiments.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Cybernetics
    DOI
    10.1109/tcyb.2019.2950560
    Additional Links
    https://ieeexplore.ieee.org/document/8906215/
    ae974a485f413a2113503eed53cd6c53
    10.1109/tcyb.2019.2950560
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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