Generalized canonical correlation analysis for disparate data fusion

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.

► We investigate a methodology for fusion and inference from multiple disparate data sources.
► The methodology separate training data into domain relation learning training data and classifier training data.
► Domain relation learning training data and classifier training data can be from completely different domains.
► Increasing the domain relation learning training data alone can improve classifier performance significantly.
► We also investigate the use of dimension reduction as an effective mean for imposing regularization.