Knowledge Transfer across Multiple Principal Component Analysis Studies

arXiv:2403.07431v1 Announce Type: cross
Abstract: Transfer learning has aroused great interest in the statistical community. In this article, we focus on knowledge transfer for unsupervised learning tasks in contrast to the supervised learning tasks in the literature. Given the transferable source populations, we propose a two-step transfer learning algorithm to extract useful information from multiple source principal component analysis (PCA) studies, thereby enhancing estimation accuracy for the target PCA task. In the first step, we integrate the shared subspace information …

abstract algorithm analysis article arxiv community contrast cs.lg extract focus knowledge literature multiple statistical stat.ml studies supervised learning tasks transfer transfer learning type unsupervised unsupervised learning