Learning of Latent Class Models by Splitting and Merging Components

Abstract

A problem in learning latent class models (also known as naive Bayes models with a hidden class variable) is that local maximum parameters are often found. This leads not only to suboptimal parameters, but also to a wrong number of classes (components) for a hidden variable