  Canonical correlation analysis (CCA) is a popular technique for learning
representations that are maximally correlated across multiple views in data. In
this paper, we extend the CCA based framework for learning a multiview mixture
model. We show that the proposed model and a set of simple heuristics yield
improvements over standard CCA, as measured in terms of performance on
downstream tasks. Our experimental results show that our correlation-based
objective meaningfully generalizes the CCA objective to a mixture of CCA
models.
