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MatTransMix: an R Package for Matrix Model-Based Clustering and Parsimonious Mixture Modeling

Author

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  • Xuwen Zhu

    (University of Alabama)

  • Shuchismita Sarkar

    (Bowing Green State University)

  • Volodymyr Melnykov

    (University of Alabama)

Abstract

Finite mixture modeling, expanded to matrix-valued data, faces several challenges. One of the major concerns is overparameterization resulting from the high number of parameters involved in a matrix mixture. In addition, an appropriate power transformation is very useful if the data are skewed. The R package MatTransMix is a new piece of software devoted to parsimonious models, based on spectral decomposition of covariance matrices, developed for fitting heterogeneous matrix-valued data providing model-based clustering results. The package implements a variety of parsimonious models obtained from various combinations of spectral decomposition and skewness parameters. The paper discusses some methodological foundations of the proposed models and elaborates the functions available in this package on carefully chosen examples.

Suggested Citation

  • Xuwen Zhu & Shuchismita Sarkar & Volodymyr Melnykov, 2022. "MatTransMix: an R Package for Matrix Model-Based Clustering and Parsimonious Mixture Modeling," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 147-170, March.
    • Handle: RePEc:spr:jclass:v:39:y:2022:i:1:d:10.1007_s00357-021-09401-9
    DOI: 10.1007/s00357-021-09401-9
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    References listed on IDEAS

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    1. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    2. Melnykov, Volodymyr & Zhu, Xuwen, 2018. "On model-based clustering of skewed matrix data," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 181-194.
    3. Sarkar, Shuchismita & Zhu, Xuwen & Melnykov, Volodymyr & Ingrassia, Salvatore, 2020. "On parsimonious models for modeling matrix data," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
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    Cited by:

    1. Leonardo Salvatore Alaimo & Francesco Amato & Filomena Maggino & Alfonso Piscitelli & Emiliano Seri, 2023. "A Comparison of Migrant Integration Policies via Mixture of Matrix-Normals," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(2), pages 473-494, January.
    2. Utkarsh J. Dang & Michael P.B. Gallaugher & Ryan P. Browne & Paul D. McNicholas, 2023. "Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 145-167, April.
    3. Donatella Vicari & Paolo Giordani, 2023. "CPclus: Candecomp/Parafac Clustering Model for Three-Way Data," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 432-465, July.

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