On Hypothesis Transfer Learning of Functional Linear Models

arXiv:2206.04277v4 Announce Type: replace-cross
Abstract: We study the transfer learning (TL) for the functional linear regression (FLR) under the Reproducing Kernel Hilbert Space (RKHS) framework, observing the TL techniques in existing high-dimensional linear regression is not compatible with the truncation-based FLR methods as functional data are intrinsically infinite-dimensional and generated by smooth underlying processes. We measure the similarity across tasks using RKHS distance, allowing the type of information being transferred tied to the properties of the imposed RKHS. Building on …

abstract arxiv cs.lg data framework functional generated hypothesis kernel linear linear regression regression space stat.ml study transfer transfer learning type