Non-Negative Transfer Learning with Consistent Inter-Domain Distribution

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Scopus Citations
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Detail(s)

Original languageEnglish
Article number9210817
Pages (from-to)1720-1724
Journal / PublicationIEEE Signal Processing Letters
Volume27
Online published1 Oct 2020
Publication statusPublished - 2020

Abstract

In this letter, we propose a novel transfer learning approach, which simultaneously exploits the intra-domain differentiation and inter-domain correlation to comprehensively solve the drawbacks many existing transfer learning methods suffer from, i.e., they either are unable to handle the negative samples or have strict assumptions on the distribution. Specifically, the sample selection strategy is introduced to handle negative samples by using the local geometry structure and the label information of source samples. Furthermore, the pseudo target label is imposed to slack the assumption on the inter-domain distribution for considering the inter-domain correlation. Then, an efficient alternating iterative algorithm is proposed to solve the formulated optimization problem with multiple constraints. The extensive experiments conducted on eleven real-world datasets show the superiority of our method over state-of-the-art approaches, i.e., our method achieves 11.23% improvement on the MNIST dataset.

Research Area(s)

  • inter-domain distribution, negative transfer, Transfer learning