Learning mixture models with the regularized latent maximum entropy principle

• Published in: IEEE transactions on neural networks Vol. 15; no. 4; pp. 903 - 916
• Main Authors: Shaojun Wang, Schuurmans, D., Fuchun Peng, Yunxin Zhao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2004
• Subjects: Algorithms; Artificial Intelligence; Computer science; Computer Simulation; Decision Support Techniques; Entropy; Estimating; Inference; Inference algorithms; Information Storage and Retrieval - methods; Information Theory; Iterative algorithms; Learning; Machine learning; Maximization; Maximum entropy; Maximum likelihood estimation; Models, Statistical; Neural networks; Neural Networks (Computer); Parametric statistics; Pattern Recognition, Automated; Probability Learning; Robustness; State estimation; Yield estimation
• DOI: 10.1109/TNN.2004.828755
• ISSN: 1045-9227

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 a posteriori 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.