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

In order to encode 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. We provide a closed-form solution to the resulting covariance-generalized optimization problem and an algorithm for its computation. We call the resulting technique – applicable to both data fusion and transfer learning – covariance-generalized MCA (CGMCA). We also demonstrate via numerical experiments that CGMCA is capable of meaningfully encoding into its maps more information than MCA.