Learning of Multivariate Beta Mixture Models via Entropy-based component splitting

Finite mixture models are progressively employed in various fields of science due to their high potential as inference engines to model multimodal and complex data. To develop them, we face some crucial issues such as choosing proper distributions with enough flexibility to well-fit the data. To learn our model, two other significant challenges, namely, parameter estimation and defining model complexity have to be addressed. Some methods such as maximum likelihood and Bayesian inference have been widely considered to tackle the first problem and both have some drawbacks such as local maxima or high computational complexity. Simultaneously, the proper number of components was determined with some approaches such as minimum message length. In this work, multivariate Beta mixture models have been deployed thanks to their flexibility and we propose a novel variational inference via an entropy-based splitting method. The performance of this approach is evaluated on real-world applications, namely, breast tissue texture classification, cytological breast data analysis, cell image categorization and age estimation.