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

Xuwen Zhu (), Shuchismita Sarkar () and Volodymyr Melnykov ()
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Xuwen Zhu: University of Alabama
Shuchismita Sarkar: Bowing Green State University
Volodymyr Melnykov: University of Alabama

Journal of Classification, 2022, vol. 39, issue 1, No 9, 147-170

Abstract: 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.

Keywords: Matrix-valued data; Model-based cluster analysis; Finite mixture models; Parsimonious models; Power transformation; R (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00357-021-09401-9

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