Mixed-transfer: Transfer learning over mixed graphs

Ben Tan, Erheng Zhong, Kwok Po NG, Qiang Yang

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

Heterogeneous transfer learning has been proposed as a new learning strategy to improve performance in a target domain by leveraging data from other heterogeneous source domains where feature spaces can be different across different domains. In order to connect two different spaces, one common technique is to bridge feature spaces by using some co-occurrence data. For example, annotated images can be used to build feature mapping from words to image features, and then applied on text-to-image knowledge transfer. However, in practice, such co-occurrence data are often from Web, e.g. Flickr, and generated by users. That means these data can be sparse and contain personal biases. Directly building models based on them may fail to provide reliable bridge. To solve these aforementioned problems, in this paper, we propose a novel algorithm named Mixed-Transfer. It is composed of three components, that is, a cross domain harmonic function to avoid personal biases, a joint transition probability graph of mixed instances and features to model the heterogeneous transfer learning problem, a random walk process to simulate the label propagation on the graph and avoid the data sparsity problem. We conduct experiments on 171 real-world tasks, showing that the proposed approach outperforms four state-of-the-art heterogeneous transfer learning algorithms.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
PublisherSociety for Industrial and Applied Mathematics Publications
Pages208-216
Number of pages9
ISBN (Electronic)9781510811515
DOIs
Publication statusPublished - 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: 24 Apr 201426 Apr 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014
Volume1

Conference

Conference14th SIAM International Conference on Data Mining, SDM 2014
Country/TerritoryUnited States
CityPhiladelphia
Period24/04/1426/04/14

Scopus Subject Areas

  • Computer Science Applications
  • Software

User-Defined Keywords

  • Co-occurrence data
  • Heterogeneous transfer learning
  • Random walk

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