Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
Shireman, E. M., Steinley, D., & Brusco, M. J. (2016). Local Optima in Mixture Modeling. Multivariate Behavioral Research. Retrieved from http://purl.flvc.org/fsu/fd/FSU_pmch_27494191
It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are presented that examine the effect of a variety of data structures on the propensity of local optima and the classification quality of the resulting solution. We show that there is a moderately strong relationship between a solution that has a high proportion of local optima and one that is poorly classified.
Shireman, E. M., Steinley, D., & Brusco, M. J. (2016). Local Optima in Mixture Modeling. Multivariate Behavioral Research. Retrieved from http://purl.flvc.org/fsu/fd/FSU_pmch_27494191