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A bootstrap‐augmented alternating expectation‐conditional maximization algorithm for mixtures of factor analyzers
Author(s) -
Shreeves Phillip,
Andrews Jeffrey L.
Publication year - 2019
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.243
Subject(s) - latent variable , expectation–maximization algorithm , cluster analysis , curse of dimensionality , benchmark (surveying) , computer science , nonparametric statistics , maximization , latent variable model , mixture model , algorithm , factor (programming language) , artificial intelligence , machine learning , mathematics , statistics , mathematical optimization , maximum likelihood , geodesy , geography , programming language
Finite mixture models are a popular approach for unsupervised machine learning tasks. Mixtures of factor analyzers assume a latent variable structure, thereby modelling the data in a lower dimensional space. Herein, we augment the traditional alternating expectation‐conditional maximization algorithm by incorporating the nonparametric bootstrap during the parameter estimation process. This augmentation is shown to improve discovery of both the true number of groups and the true latent dimensionality through simulations, while also showing superior clustering performance on benchmark data sets.