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Journal of Classification

Erschienen in:

22.09.2021

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

verfasst von: Xuwen Zhu, Shuchismita Sarkar, Volodymyr Melnykov

Erschienen in: Journal of Classification | Ausgabe 1/2022

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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.

Vorheriger Artikel Interaction Identification and Clique Screening for Classification with Ultra-high Dimensional Discrete Features

Nächster Artikel Chimeral Clustering

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Titel: MatTransMix: an R Package for Matrix Model-Based Clustering and Parsimonious Mixture Modeling
verfasst von: Xuwen Zhu
Shuchismita Sarkar
Volodymyr Melnykov
Publikationsdatum: 22.09.2021
Verlag: Springer US
Erschienen in: Journal of Classification / Ausgabe 1/2022
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI: https://doi.org/10.1007/s00357-021-09401-9

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