Matching Component Analysis for Transfer Learning
arXiv:1909.01797
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.
Submitted to SIAM Journal on Mathematics of Data Science (SIMODS)