Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)
Alan L. Yuille
This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We relate the algorithm to the stochastic approxi- mation literature. This enables us to specify conditions under which the algorithm is guaranteed to converge to the optimal solution (with proba- bility 1). This includes necessary and sufficient conditions for the solu- tion to be unbiased.