Severity of local maxima for the EM algorithm: experiences with hierarchical latent class models
Yi Wang, Nevin Lianwen Zhang · 2006
It is common knowledge that the EM algorithm can be trapped at local maxima and consequently fails to reach global maxima. We empirically investigate the severity of this problem in the context of hierarchical latent class (HLC) models. Our experiments were run on HLC models where dependency between neighboring variables is strong. (The reason for focusing on this class of models will be made clear in the main text.) We first ran EM from randomly generated single starting points, and observed that (1) the probability of hitting global maxima is generally high, (2) it increases with the strength of dependency and sample sizes, and (3) it decreases with the amount of extreme probability values. We also observed that, at high dependence strength levels, local maxima are far apart from global ones in terms of likelihoods. Those imply that local maxima can be reliably avoided by running EM from a few starting points and hence are not a serious issue. This is confirmed by our second set of experiments. 1