MDL-Based Selection of the Number of Components in Mixture Models for Pattern Classification

Label switching is one of the fundamental problems for Bayesian mixture model analysis. Due to the permutation invariance of the mixture posterior, we can consider that the posterior of a m-component mixture model is a mixture distribution with m! ...

Model-based clustering associates each component of a finite mixture distribution to a group or cluster. Therefore, an underlying implicit assumption is that a one-to-one correspondence exists between mixture components and clusters. In applications ...

Finite mixture models (FMM) are flexible models with varying uses such as density estimation, clustering, classification, modeling heterogeneity, model averaging, and handling missing data. Expectation maximization (EM) algorithm can learn the maximum ...