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    Regularized matrix data clustering and its application to image analysis

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    Thumbnail
    Name:
    biom.13354.pdf
    Size:
    611.1Kb
    Format:
    PDF
    Description:
    Accepted Article
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    Type
    Article
    Authors
    Gao, Xu cc
    Shen, Weining cc
    Zhang, Liwen
    Hu, Jianhua cc
    Fortin, Norbert J.
    Frostig, Ron D.
    Ombao, Hernando cc
    KAUST Department
    Biostatistics Group
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2020-08-24
    Embargo End Date
    2021-07-21
    Submitted Date
    2019-08-14
    Permanent link to this record
    http://hdl.handle.net/10754/664640
    
    Metadata
    Show full item record
    Abstract
    We propose a novel regularized mixture model for clustering matrix-valued data. The proposed method assumes a separable covariance structure for each cluster and imposes a sparsity structure (e.g., low rankness, spatial sparsity) for the mean signal of each cluster. We formulate the problem as a finite mixture model of matrix-normal distributions with regularization terms, and then develop an EM-type of algorithm for efficient computation. In theory, we show that the proposed estimators are strongly consistent for various choices of penalty functions. Simulation and two applications on brain signal studies confirm the excellent performance of the proposed method including a better prediction accuracy than the competitors and the scientific interpretability of the solution.
    Citation
    Gao, X., Shen, W., Zhang, L., Hu, J., Fortin, N. J., Frostig, R. D., & Ombao, H. (2020). Regularized matrix data clustering and its application to image analysis. Biometrics. doi:10.1111/biom.13354
    Sponsors
    The authors thank the editor, the associate editor and two referees for their constructive and helpful comments on the earlier version of this paper. Shen’s research was partially supported by the Simons Foundation Award 512620 and the NSF Grant DMS 1509023. Hu’s effort was partially supported by the National Institute of Health Grants R01AI143886, R01CA219896,and CCSG P30 CA013696. Fortin’s research was supported by NIH grant R01-MH115697,NSF awards IOS-1150292 and BCS-1439267, and Whitehall Foundation award 2010-05-84.Frostig’s research was partially supported by the Leducq Foundation.
    Publisher
    Wiley
    Journal
    Biometrics
    DOI
    10.1111/biom.13354
    arXiv
    1808.01749
    Additional Links
    https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13354
    ae974a485f413a2113503eed53cd6c53
    10.1111/biom.13354
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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