On Contrastive Divergence Learning
Abstract
Maximum-likelihood (ML) learning of Markov random fields is challenging because it requires estimates of averages that have an exponential number of terms. Markov chain Monte Carlo methods typically take a long time to converge on unbiased estimates, but Hinton (2002) showed that if the Markov chain is only run for a few steps, the learning can still work well and it approximately minimizes a di#erent function called "contrastive divergence" (CD). CD learning has been successfully applied to various types of random fields. Here, we study the properties of CD learning and show that it provides biased estimates in general, but that the bias is typically very small. Fast CD learning can therefore be used to get close to an ML solution and slow ML learning can then be used to fine-tune the CD solution.
Cite
Text
Carreira-Perpiñán and Hinton. "On Contrastive Divergence Learning." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.Markdown
[Carreira-Perpiñán and Hinton. "On Contrastive Divergence Learning." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.](https://mlanthology.org/aistats/2005/carreiraperpinan2005aistats-contrastive/)BibTeX
@inproceedings{carreiraperpinan2005aistats-contrastive,
title = {{On Contrastive Divergence Learning}},
author = {Carreira-Perpiñán, Miguel Á. and Hinton, Geoffrey},
booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics},
year = {2005},
pages = {33-40},
volume = {R5},
url = {https://mlanthology.org/aistats/2005/carreiraperpinan2005aistats-contrastive/}
}