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