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

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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.

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Authors and Affiliations

  1. Department of Information Systems, Statistics, and Management Science, University of Alabama, Tuscaloosa, AL, 35487, USA

    Xuwen Zhu & Volodymyr Melnykov

  2. Department of Applied Statistics and Operation Research, Bowing Green State University, Bowling Green, OH, 43403, USA

    Shuchismita Sarkar

Authors
  1. Xuwen Zhu
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  2. Shuchismita Sarkar
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  3. Volodymyr Melnykov
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Correspondence to Xuwen Zhu.

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Zhu, X., Sarkar, S. & Melnykov, V. MatTransMix: an R Package for Matrix Model-Based Clustering and Parsimonious Mixture Modeling. J Classif 39, 147–170 (2022). https://doi.org/10.1007/s00357-021-09401-9

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