  Manifold matching works to identify embeddings of multiple disparate data
spaces into the same low-dimensional space, where joint inference can be
pursued. It is an enabling methodology for fusion and inference from multiple
and massive disparate data sources. In this paper we focus on a method called
Canonical Correlation Analysis (CCA) and its generalization Generalized
Canonical Correlation Analysis (GCCA), which belong to the more general Reduced
Rank Regression (RRR) framework. We present an efficiency investigation of CCA
and GCCA under different training conditions for a particular text document
classification task.
