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

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).

Random perturbation and matrix sparsification and completion

We discuss general perturbation inequalities when the perturbation is random. As applications, we obtain several new results concerning two important problems: matrix sparsification and matrix completion.