Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning
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.
Cite
@article{arxiv.2110.13194,
title = {Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning},
author = {Nick Lorenzo and Sean O'Rourke and Theresa Scarnati},
journal= {arXiv preprint arXiv:2110.13194},
year = {2022}
}
Comments
v3: Made major organizational changes; added numerical results; eliminated statement and proof of lemma in favor of offering a solution achieving a cited bound