@inproceedings{Ghahramani2007BayesianNL,
title={Bayesian nonparametric latent feature models},
author={Zoubin Ghahramani and Thomas L. Griffiths},
year={2007}
}

We describe a flexible nonparametric approach to latent variable modelling in which the number of latent variables is unbounded. This approach is based on a probability distribution over equivalence classes of binary matrices with a finite number of rows, corresponding to the data points, and an unbounded number of columns, corresponding to the latent variables. Each data point can be associated with a subset of the possible latent variables, which we refer to as the latent features of that… CONTINUE READING