Convex Hidden Markov Models

Speaker: Linli Xu

In this talk, I will discuss a new unsupervised algorithm for training hidden Markov models that is convex and avoids the use of EM. The idea is to formulate an unsupervised version of maximum margin Markov networks (M3Ns) that can be trained via semidefinite programming. This extends our recent work on unsupervised support vector machines. The result is a discriminative training criterion for hidden Markov models that remains unsupervised and does not create local minima. Experimental results show that the convex discriminative procedure can produce better conditional models than conventional Baum-Welch (EM) training.