Learning mixture models with the regularized latent maximum entropy principle.
This paper presents a new approach to estimating mixture models based on a recent inference principle we have proposed: the latent maximum entropy principle (LME). LME is different from Jaynes' maximum entropy principle, standard maximum likelihood, and maximum aposteriori probability estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the expectation maximization (EM) algorithm can be developed. We show that a regularized version of LME (RLME), is effective at estimating mixture models. It generally yields better results than plain LME, which in turn is often better than maximum likelihood and maximum a posterior estimation, particularly when inferring latent variable models from small amounts of data.
Department of Computing Science, University of Alberta, Alberta T6G 2E8, Canada.
Shaojun
S
Wang
Dale
D
Schuurmans
Fuchun
F
Peng
Yunxin
Y
Zhao
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D019277:Entropy; D016247:Information Storage and Retrieval / Q000379:methods*; D007257:Information Theory; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011337:Probability Learning