Component-wise parameter smoothing for learning mixture models

In this paper, we propose a novel component-wise smoothing algorithm that constructs a hierarchy (or family) of smoothened log-likelihood surfaces. Our approach first smoothens the likelihood function and then applies the EM algorithm to obtain a promising solution on this smoothened surface. Using the most promising solutions as initial guesses, the EM algorithm is applied again on the original likelihood. This effective optimization procedure eliminates extensive search in the non-promising regions of the parameter space. Empirical results on some standard datasets show the reduction of the number of local maxima and improvements in the log-likelihood values.