Restricting exchangeable nonparametric distributions

Distributions over matrices with exchangeable rows and infinitely many columns are useful in constructing nonparametric latent variable models. However, the distribution impliedby 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 aclass of exchangeable nonparametric priors obtained by restricting the domain ofexisting 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.