  Outlier detection amounts to finding data points that differ significantly
from the norm. Classic outlier detection methods are largely designed for
single data type such as continuous or discrete. However, real world data is
increasingly heterogeneous, where a data point can have both discrete and
continuous attributes. Handling mixed-type data in a disciplined way remains a
great challenge. In this paper, we propose a new unsupervised outlier detection
method for mixed-type data based on Mixed-variate Restricted Boltzmann Machine
(Mv.RBM). The Mv.RBM is a principled probabilistic method that models data
density. We propose to use \emph{free-energy} derived from Mv.RBM as outlier
score to detect outliers as those data points lying in low density regions. The
method is fast to learn and compute, is scalable to massive datasets. At the
same time, the outlier score is identical to data negative log-density up-to an
additive constant. We evaluate the proposed method on synthetic and real-world
datasets and demonstrate that (a) a proper handling mixed-types is necessary in
outlier detection, and (b) free-energy of Mv.RBM is a powerful and efficient
outlier scoring method, which is highly competitive against state-of-the-arts.
