Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates

We present a split and merge EM algorithm to overcome the local maximum problem in Gaussian mixture density estimation. Nonglobal maxims often involve having too many Gaussians in one part of the space and too few in another, widely separated part of the space. To escape from such configurations we repeatedly perform split and merge operations using a new criterion for efficiently selecting the split and merge candidates. Experimental results on synthetic and real data show the effectiveness of using the split and merge operations to improve the likelihood of both the training data and of held-out test data.