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).
翻译:为了在数据聚合和转移学习应用程序中将更多的统计资料编码,我们对匹配组件分析(MCA)的转移学习技术实行普遍共变限制。在证明半单翼限制的追踪最大limma后,我们为由此产生的共变优化问题开发了封闭式解决办法,并为计算提供算法。我们称之为既适用于数据聚合又适用于转移学习的技术,即共同变式MCA(CCA)。