Online heterogeneous transfer learning by knowledge transition

Hanrui Wu, Yuguang Yan, Yuzhong Ye, Huaqing Min, Kwok Po NG, Qingyao Wu

Research output: Contribution to journalJournal articlepeer-review

26 Citations (Scopus)


In this article, we study the problem of online heterogeneous transfer learning, where the objective is to make predictions for a target data sequence arriving in an online fashion, and some offline labeled instances from a heterogeneous source domain are provided as auxiliary data. The feature spaces of the source and target domains are completely different, thus the source data cannot be used directly to assist the learning task in the target domain. To address this issue, we take advantage of unlabeled co-occurrence instances as intermediate supplementary data to connect the source and target domains, and perform knowledge transition from the source domain into the target domain. We propose a novel online heterogeneous transfer learning algorithm called Online Heterogeneous Knowledge Transition (OHKT) for this purpose. In OHKT, we first seek to generate pseudo labels for the co-occurrence data based on the labeled source data, and then develop an online learning algorithm to classify the target sequence by leveraging the co-occurrence data with pseudo labels. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.

Original languageEnglish
Article numberY
JournalACM Transactions on Intelligent Systems and Technology
Issue number3
Publication statusPublished - May 2019

Scopus Subject Areas

  • Theoretical Computer Science
  • Artificial Intelligence

User-Defined Keywords

  • Co-occurrence data
  • Heterogeneous transfer learning
  • Online learning
  • Transitive transfer learning


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