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    Robust Two-Layer Partition Clustering of Sparse Multivariate Functional Data

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    2204.12135.pdf
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    Type
    Preprint
    Authors
    Qu, Zhuo
    Dai, Wenlin
    Genton, Marc G. cc
    KAUST Department
    Statistics
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Statistics Program
    Date
    2022-04-26
    Permanent link to this record
    http://hdl.handle.net/10754/676617
    
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    Abstract
    In this work, a novel elastic time distance for sparse multivariate functional data is proposed. This concept serves as the foundation for clustering functional data with various time measurements per subject. Subsequently, a robust two-layer partition clustering is introduced. With the proposed distance, our approach not only is applicable to both complete and imbalanced multivariate functional data but also is resistant to outliers and capable of detecting outliers that do not belong to any clusters. The classical distance-based clustering methods such as K-medoids and agglomerative hierarchical clustering are extended to the sparse multivariate functional case based on our proposed distance. Numerical experiments on the simulated data highlight that the performance of the proposed algorithm is superior to the performances of the existing model-based and extended distance-based methods. Using Northwest Pacific cyclone track data as an example, we demonstrate the effectiveness of the proposed approach. The code is available online for readers to apply our clustering method and replicate our analyses.
    Publisher
    arXiv
    arXiv
    2204.12135
    Additional Links
    https://arxiv.org/pdf/2204.12135.pdf
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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