SMEM algorithm is not fully compatible with maximum-likelihood framework.
The expectation-maximization (EM) algorithm with split-and-merge operations (SMEM algorithm) proposed by Ueda, Nakano, Ghahramani, and Hinton (2000) is a nonlocal searching method, applicable to mixture models, for relaxing the local optimum property of the EM algorithm. In this article, we point out that the SMEM algorithm uses the acceptance-rejection evaluation method, which may pick up a distribution with smaller likelihood, and demonstrate that an increase in likelihood can then be guaranteed only by comparing log likelihoods.
Minagawa
Akihiro
A
Graduate School of Engineering, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397 Japan. akihiro@eei.metro-u.ac.jp
Tagawa
Norio
N
Tanaka
Toshiyuki
T