  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.
