Matching Component Analysis for Transfer Learning

TL;DR

This paper presents a novel Procrustes-type method called matching component analysis designed to identify transferable data components, supported by theoretical analysis and numerical experiments demonstrating its effectiveness.

Contribution

The paper introduces matching component analysis, a new method with theoretical sample complexity results, tailored for transfer learning applications.

Findings

The method effectively isolates transferable components.

Theoretical sample complexity bounds are established.

Numerical experiments confirm practical utility.

Abstract

We introduce a new Procrustes-type method called matching component analysis to isolate components in data for transfer learning. Our theoretical results describe the sample complexity of this method, and we demonstrate through numerical experiments that our approach is indeed well suited for transfer learning.