  How can we correlate neural activity in the human brain as it responds to
words, with behavioral data expressed as answers to questions about these same
words? In short, we want to find latent variables, that explain both the brain
activity, as well as the behavioral responses. We show that this is an instance
of the Coupled Matrix-Tensor Factorization (CMTF) problem. We propose
Scoup-SMT, a novel, fast, and parallel algorithm that solves the CMTF problem
and produces a sparse latent low-rank subspace of the data. In our experiments,
we find that Scoup-SMT is 50-100 times faster than a state-of-the-art algorithm
for CMTF, along with a 5 fold increase in sparsity. Moreover, we extend
Scoup-SMT to handle missing data without degradation of performance. We apply
Scoup-SMT to BrainQ, a dataset consisting of a (nouns, brain voxels, human
subjects) tensor and a (nouns, properties) matrix, with coupling along the
nouns dimension. Scoup-SMT is able to find meaningful latent variables, as well
as to predict brain activity with competitive accuracy. Finally, we demonstrate
the generality of Scoup-SMT, by applying it on a Facebook dataset (users,
friends, wall-postings); there, Scoup-SMT spots spammer-like anomalies.
