  This paper presents a Bayesian nonparametric latent feature model specially
suitable for exploratory analysis of high-dimensional count data. We perform a
non-negative doubly sparse matrix factorization that has two main advantages:
not only we are able to better approximate the row input distributions, but the
inferred topics are also easier to interpret. By combining the three-parameter
and restricted Indian buffet processes into a single prior, we increase the
model flexibility, allowing for a full spectrum of sparse solutions in the
latent space. We demonstrate the usefulness of our approach in the analysis of
countries' economic structure. Compared to other approaches, empirical results
show our model's ability to give easy-to-interpret information and better
capture the underlying sparsity structure of data.
