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

TL;DR

This paper introduces covariance-generalized MCA (CGMCA), a novel data fusion and transfer learning method that encodes richer statistical information through a new covariance constraint, with a closed-form solution and demonstrated effectiveness.

Contribution

The paper proposes a new covariance-generalized optimization for MCA, providing a closed-form solution and demonstrating improved information encoding in data fusion and transfer learning.

Findings

CGMCA encodes more statistical information than traditional MCA.

The authors provide a closed-form solution for the covariance-generalized optimization.

Numerical experiments show CGMCA's effectiveness in 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.