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.

引入广义协方差约束来编码数据融合和迁移学习应用中的额外统计信息，截至目前的最佳技术 CGMCA 可以比 MCA 更有意义地编码地图，并提供了计算算法。

数据融合和迁移学习的协方差广义匹配成分分析

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

We introduce a new Procrustes-type method called matching component analysis
to isolate components in data for transfer learning. Our theoretical results
describe the sample complexity of this method, and we demonstrate through
numerical experiments that our approach is indeed well suited for transfer
learning.

介绍了一种名为匹配组件分析的新型 Procrustes 方法，可通过转移学习来分离数据中的组件；理论结果表明其采样复杂度，我们通过数值实验证明了我们的方法确实非常适合于转移学习。