Williamson, S and Ghahramani, Z and MacEachern, SN and Xing, EP Restricting exchangeable nonparametric distributions. (Unpublished)Full text not available from this repository.
Distributions over exchangeable matrices with infinitely many columns, such as the Indian buffet process, are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly- suited for many modeling tasks. In this paper, we propose a class of exchangeable nonparametric priors obtained by restricting the domain of existing models. Such models allow us to specify the distribution over the number of features per data point, and can achieve better performance on data sets where the number of features is not well-modeled by the original distribution.
|Uncontrolled Keywords:||stat.ME stat.ME stat.ML|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 18:27|
|Last Modified:||16 Jan 2017 11:08|