A combined method for finding best starting points for optimisation in bernoulli mixture models

Mixture models are frequently used to classify data. They are likelihood based models, and the maximum likelihood estimates of parameters are often obtained using the expectation maximization (EM) algorithm. However, multimodality of the likelihood surface means that poorly chosen starting points for optimisation may lead to only a local maximum, not a global maximum. In this paper, different methods of choosing starting points will be evaluated and compared, mainly via simulated data and then we will introduce a procedure which will make intelligent choices of possible starting points and fast evaluations of their usefulness.