An effective strategy for initializing the EM algorithm in finite mixture models

Finite mixture models represent one of the most popular tools for modeling heterogeneous data. The traditional approach for parameter estimation is based on maximizing the likelihood function. Direct optimization is often troublesome due to the complex likelihood structure. The expectation-maximization algorithm proves to be an effective remedy that alleviates this issue. The solution obtained by this procedure is entirely driven by the choice of starting parameter values. This highlights the importance of an effective initialization strategy. Despite efforts undertaken in this area, there is no uniform winner found and practitioners tend to ignore the issue, often finding misleading or erroneous results. In this paper, we propose a simple yet effective tool for initializing the expectation-maximization algorithm in the mixture modeling setting. The idea is based on model averaging and proves to be efficient in detecting correct solutions even in those cases when competitors perform poorly. The utility of the proposed methodology is shown through comprehensive simulation study and applied to a well-known classification dataset with good results.