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  • Low-rank hypergraph feature selection for multi-output regression

    Author(s)
    Zhu, Xiaofeng
    Hu, Rongyao
    Lei, Cong
    Thung, Kim Han
    Zheng, Wei
    Wang, Can
    Griffith University Author(s)
    Wang, Can
    Year published
    2019
    Metadata
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    Abstract
    Current multi-output regression method usually ignores the relationship among response variables, and thus it is challenging to obtain an effective coefficient matrix for predicting the response variables with the features. We address these problems by proposing a novel multi-output regression method, which combines sparse feature selection and low-rank linear regression in a unified framework. Specifically, we first utilize a hypergraph Laplacian regularization term to preserve the high-order structure among all the samples, and then use a low-rank constraint to respectively discover the hidden structure among the response ...
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    Current multi-output regression method usually ignores the relationship among response variables, and thus it is challenging to obtain an effective coefficient matrix for predicting the response variables with the features. We address these problems by proposing a novel multi-output regression method, which combines sparse feature selection and low-rank linear regression in a unified framework. Specifically, we first utilize a hypergraph Laplacian regularization term to preserve the high-order structure among all the samples, and then use a low-rank constraint to respectively discover the hidden structure among the response variables and explore the relationship among different features in a least square regression framework. As a result, we integrate subspace learning with sparse feature selection to select useful features for multi-output regression. We tested our proposed method using several public data sets, and the experimental results showed that our method outperformed other comparison methods.
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    Journal Title
    World Wide Web
    DOI
    https://doi.org/10.1007/s11280-017-0514-5
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Data management and data science
    Distributed computing and systems software
    Distributed computing and systems software not elsewhere classified
    Information systems
    Publication URI
    http://hdl.handle.net/10072/371066
    Collection
    • Journal articles

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