  We present a new approach for learning compact and intuitive distributed
representations with binary encoding. Rather than summing up expert votes as in
products of experts, we employ for each variable the opinion of the most
reliable expert. Data points are hence explained through a partitioning of the
variables into expert supports. The partitions are dynamically adapted based on
which experts are active. During the learning phase we adopt a smoothed version
of this model that uses separate mixtures for each data dimension. In our
experiments we achieve accurate reconstructions of high-dimensional data points
with at most a dozen experts.
