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

arXiv: https://arxiv.org/abs/2110.13194 · HTML: https://mlsanity.com/paper/2110.13194/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Built on by (most cited first, 1 of 1)

- [Star-Graph Multimodal Matching Component Analysis for Data Fusion and Transfer Learning](https://mlsanity.com/paper/2210.02590/) (2022)

## Abstract (arXiv)

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.