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.
|Title of host publication||Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI 2011)|
|Number of pages||8|
|Publication status||Published - 2011|