A Novel Transfer Learning Method Based on Common Space Mapping and Weighted Domain Matching

Ru-Ze Liang, Wei Xie, Weizhi Li, Hongqi Wang, Jim Jing-Yan Wang, Lisa Taylor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

In this paper, we propose a novel learning framework for the problem of domain transfer learning. We map the data of two domains to one single common space, and learn a classifier in this common space. Then we adapt the common classifier to the two domains by adding two adaptive functions to it respectively. In the common space, the target domain data points are weighted and matched to the target domain in term of distributions. The weighting terms of source domain data points and the target domain classification responses are also regularized by the local reconstruction coefficients. The novel transfer learning framework is evaluated over some benchmark cross-domain data sets, and it outperforms the existing state-of-the-art transfer learning methods.
Original languageEnglish (US)
Title of host publication2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781509044597
DOIs
StatePublished - Jan 17 2017

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