Machine Learning and Friends Lunch

Latent Maximum Entropy: A new approach to mixture model estimation

Abstract

We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes' maximum entropy principle and from standard maximum likelihood estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the EM algorithm can be developed. Our experiments show that estimation based on LME generally yields better results than maximum likelihood estimation, particularly when inferring latent variable models from small amounts of data.

Joint work with Shaojun Wang, Dale Schuurmans, and Yunxin Zhao

Back to ML Lunch home