  Latent feature modeling allows capturing the latent structure responsible for
generating the observed properties of a set of objects. It is often used to
make predictions either for new values of interest or missing information in
the original data, as well as to perform data exploratory analysis. However,
although there is an extensive literature on latent feature models for
homogeneous datasets, where all the attributes that describe each object are of
the same (continuous or discrete) nature, there is a lack of work on latent
feature modeling for heterogeneous databases. In this paper, we introduce a
general Bayesian nonparametric latent feature model suitable for heterogeneous
datasets, where the attributes describing each object can be either discrete,
continuous or mixed variables. The proposed model presents several important
properties. First, it accounts for heterogeneous data while keeping the
properties of conjugate models, which allow us to infer the model in linear
time with respect to the number of objects and attributes. Second, its Bayesian
nonparametric nature allows us to automatically infer the model complexity from
the data, i.e., the number of features necessary to capture the latent
structure in the data. Third, the latent features in the model are
binary-valued variables, easing the interpretability of the obtained latent
features in data exploratory analysis. We show the flexibility of the proposed
model by solving both prediction and data analysis tasks on several real-world
datasets. Moreover, a software package of the GLFM is publicly available for
other researcher to use and improve it.
