Graph-based transfer learning

Jingrui He, Yan Liu, Richard Lawrence

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

13 Scopus citations

Abstract

Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. In this paper, we propose a graph-based transfer learning framework. It propagates the label information from the source domain to the target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bi-partite graph. Our framework is semi-supervised and non-parametric in nature and thus more flexible. We also develop an iterative algorithm so that our framework is scalable to large-scale applications. It enjoys the theoretical property of convergence. Compared with existing transfer learning methods, the proposed framework propagates the label information to both the features irrelevant to the source domain and the unlabeled examples in the target omain via the common features in a principled way. Experimental results on 3 real data sets demonstrate the effectiveness of our algorithm.

Original languageEnglish (US)
Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Pages937-946
Number of pages10
DOIs
StatePublished - Dec 1 2009
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: Nov 2 2009Nov 6 2009

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

ConferenceACM 18th International Conference on Information and Knowledge Management, CIKM 2009
CountryChina
CityHong Kong
Period11/2/0911/6/09

Keywords

  • Graph-based
  • Transfer learning

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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