Simultaneous Bayesian Clustering and Model Selection with Mixture of Robust Factor Analyzers

Shan Feng (), Wenxian Xie and Yufeng Nie ()
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Shan Feng: School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
Wenxian Xie: School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
Yufeng Nie: School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China

Mathematics, 2024, vol. 12, issue 7, 1-23

Abstract: Finite Gaussian mixture models are powerful tools for modeling distributions of random phenomena and are widely used for clustering tasks. However, their interpretability and efficiency are often degraded by the impact of redundancy and noise, especially on high-dimensional datasets. In this work, we propose a generative graphical model for parsimonious modeling of the Gaussian mixtures and robust unsupervised learning. The model assumes that the data are generated independently and identically from a finite mixture of robust factor analyzers, where the features’ salience is adjusted by an active set of latent factors to allow a violation of the local independence assumption. For the model inference, we propose a structured variational Bayes inference framework to realize simultaneous clustering, model selection and outlier processing. Performance of the proposed algorithm is evaluated by conducting experiments on artificial and real-world datasets. Moreover, an application on the high-dimensional machine learning task of handwritten alphabet recognition is introduced.

Keywords: Bayesian inference; feature selection; mixture of factor analyzers; robust clustering; structured variational Bayes (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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