Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning

In order to allow for the encoding of additional statistical information in data fusion and transfer learning applications, we introduce a generalized covariance constraint for the matching component analysis (MCA) transfer learning technique. After proving a semi-orthogonally constrained trace maximization lemma, we develop a closed-form solution to the resulting covariance-generalized optimization problem and provide an algorithm for its computation. We call this technique -- applicable to both data fusion and transfer learning -- covariance-generalized MCA (CGMCA).