  Latent variable models are an elegant framework for capturing rich
probabilistic dependencies in many applications. However, current approaches
typically parametrize these models using conditional probability tables, and
learning relies predominantly on local search heuristics such as Expectation
Maximization. Using tensor algebra, we propose an alternative parameterization
of latent variable models (where the model structures are junction trees) that
still allows for computation of marginals among observed variables. While this
novel representation leads to a moderate increase in the number of parameters
for junction trees of low treewidth, it lets us design a local-minimum-free
algorithm for learning this parameterization. The main computation of the
algorithm involves only tensor operations and SVDs which can be orders of
magnitude faster than EM algorithms for large datasets. To our knowledge, this
is the first provably consistent parameter learning technique for a large class
of low-treewidth latent graphical models beyond trees. We demonstrate the
advantages of our method on synthetic and real datasets.
