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arXiv · 2110.13194

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

Nick Lorenzo·Sean O'Rourke·Theresa Scarnati
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Abstract

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.

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Nick Lorenzo, Sean O'Rourke, Theresa Scarnati. 2021-10-25. Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning. https://arxiv.org/abs/2110.13194

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