  We show that the Bregman divergence provides a rich framework to estimate
unnormalized statistical models for continuous or discrete random variables,
that is, models which do not integrate or sum to one, respectively. We prove
that recent estimation methods such as noise-contrastive estimation, ratio
matching, and score matching belong to the proposed framework, and explain
their interconnection based on supervised learning. Further, we discuss the
role of boosting in unsupervised learning.
