Obtaining EM Initial Points by Using the Primitive Initial Point and Subsampling Strategy

The EM algorithm is an efficient algorithm to obtain the ML estimate for incomplete data, but has the local optimality problem. The deterministic annealing EM (DAEM) algorithm was once proposed to solve this problem, which begins a search from the primitive initial point. Then the mes-EM algorithm was proposed: a variant of the m-EM algorithm which begins the multiple-token EM search from the primitive initial point. The mes-EM could obtain excellent solutions in compensation for rather high computing cost. This paper proposes a lighter version of the mes-EM algorithm using the subsampling strategy and evaluates its performance.