On variable selection in matrix mixture modelling
Yang Wang, Volodymyr Melnykov · Stat · 2020
Summary Finite mixture models are widely used for cluster analysis, including clustering matrix data. Nowadays, high‐dimensional matrix observations arise in a variety of fields. It is known that irrelevant variables can severely affect the performance of clustering procedures. Therefore, it is important to develop algorithms capable of excluding irrelevant variables and focusing on informative attributes in order to achieve good clustering results. Several variable selection approaches have been proposed in the multivariate framework. We introduce and study a variable selection procedure that can be applied in the matrix‐variate context. The methodological developments are supported by several simulation studies and application to real‐life data sets, with good results.