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    An Outlyingness Matrix for Multivariate Functional Data Classification

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    SS-2016-0537_na.pdf
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    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Dai, Wenlin
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2018
    Online Publication Date
    2018
    Print Publication Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/626367
    
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    Abstract
    The classification of multivariate functional data is an important task in scientific research. Unlike point-wise data, functional data are usually classified by their shapes rather than by their scales. We define an outlyingness matrix by extending directional outlyingness, an effective measure of the shape variation of curves that combines the direction of outlyingness with conventional statistical depth. We propose two classifiers based on directional outlyingness and the outlyingness matrix, respectively. Our classifiers provide better performance compared with existing depth-based classifiers when applied on both univariate and multivariate functional data from simulation studies. We also test our methods on two data problems: speech recognition and gesture classification, and obtain results that are consistent with the findings from the simulated data.
    Citation
    Dai W, Genton MG (2018) An Outlyingness Matrix for Multivariate Functional Data Classification. Statistica Sinica. Available: http://dx.doi.org/10.5705/ss.202016.0537.
    Sponsors
    The authors thank the editor, the associate editor and the two referees for their constructive comments that led to a substantial improvement of the paper. The work of Wenlin Dai and Marc G. Genton was supported by King Abdullah University of Science and Technology (KAUST).
    Publisher
    Statistica Sinica (Institute of Statistical Science)
    Journal
    Statistica Sinica
    DOI
    10.5705/ss.202016.0537
    arXiv
    1704.02568
    Additional Links
    http://www3.stat.sinica.edu.tw/ss_newpaper/SS-2016-0537_na.pdf
    ae974a485f413a2113503eed53cd6c53
    10.5705/ss.202016.0537
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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