The /spl alpha/-EM learning and its cookbook: from mixture-of-expert neural networks to movie random field

Abstract

The /spl alpha/-EM algorithm is a proper extension of the traditional log-EM algorithm. This new algorithm is based on the /spl alpha/-logarithm, while the traditional one uses the logarithm. The case of /spl alpha/=-1 corresponds to the log-EM algorithm. Since the speed of the /spl alpha/-EM algorithm was reported for learning problems, this paper shows that closed-form E-steps can be obtained for a wide class of problems. There is a set of common techniques. That is, a cookbooks for the /spl alpha/-EM algorithm is presented. The recipes include unsupervised neural networks, supervised neural networks for various gating, hidden Markov models and Markov random fields for moving object segmentation. Reasoning for the speedup is also given.