Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning
This is an incremental improvement for data fusion and transfer learning applications.
The authors tackled the problem of encoding additional statistical information in data fusion and transfer learning by introducing a covariance constraint to matching component analysis, resulting in CGMCA, which numerical experiments showed could encode more information than MCA.
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.