Multiview Semi-supervised Learning

  • Shiliang Sun
  • Liang Mao
  • Ziang Dong
  • Lidan Wu


Semi-supervised learning is concerned with such learning scenarios where only a small portion of training data are labeled. In multiview settings, unlabeled data can be used to regularize the prediction functions, and thus to reduce the search space. In this chapter, we introduce two categories of multiview semi-supervised learning methods. The first one contains the co-training style methods, where the prediction functions from different views are trained through their own objective, and each prediction function is improved by the others. The second one contains the co-regularization style methods, where a single objective function exists for the prediction functions from different views to be trained simultaneously.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

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