Driven by the dramatic growth of data both in terms of the size and sources, learning from heterogeneous data is emerging as an important research direction for many real applications. One of the biggest challenges of this type of problem is how to meaningfully integrate heterogeneous data to considerably improve the generality and quality of the learning model. In this paper, we first present a unified learning framework that aims to leverage the structural information from two types of data heterogeneity: view heterogeneity (as in multi-view learning) and worker heterogeneity (as in crowdsourcing). The objective follows the principles of view consistency and worker consensus by minimizing the loss term with a regularized prediction tensor. We then propose to relax and solve the optimization framework with an iterative updating method. We also prove that the gradient of the most time-consuming updating block is separable with respect to the workers, which leads to a randomized algorithm with faster speed and better convergence. Finally, we compare the proposed method with several state-of-the-arts and demonstrate its effectiveness on various data sets.

Original languageEnglish (US)
Title of host publicationProceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
EditorsGeorge Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781538638347
StatePublished - Dec 15 2017
Event17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States
Duration: Nov 18 2017Nov 21 2017

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other17th IEEE International Conference on Data Mining, ICDM 2017
Country/TerritoryUnited States
CityNew Orleans


  • Crowdsourcing
  • Heterogeneous Learning
  • Multi-view Learning
  • Optimization

ASJC Scopus subject areas

  • Engineering(all)


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