Multiview Active Learning

  • Shiliang SunEmail author
  • Liang Mao
  • Ziang Dong
  • Lidan Wu


Active learning is proposed based on the fact that manually labeled examples are expensive, thus it picks the most informative points to label so as to improve the learning efficiency. Combined with multiview learning algorithm, it constructs multiple learners to select contention points among different views. In this chapter, we introduce five multiview active learning algorithms as examples. At first, we introduce co-testing, the first algorithm applying active learning to multiview learning, and discuss how to process the contradiction between multiple learners. Bayesian co-training is proposed under the mutual information framework, which considers the unobserved labels as latent variables and marginalizes them out. We focus on multiview multi-learner learning active learning, which introduces the ambiguity of an example to measure its confidence. In the situation that active learning with extremely sparse labeled examples, there is a detailed derivation of CCA in two view. At last, we retell a practical active learning algorithm combined with semi-supervised learning. Besides, there are other methods briefly mentioned at the end of this chapter.


  1. Muslea I, Minton S, Knoblock CA (2002) Active + semi-supervised learning = robust multi-view learning. In: Proceedings of the 9th international conference on machine learning, pp 435–442Google Scholar
  2. Muslea I, Minton S, Knoblock CA (2006) Active learning with multiple views. J Artif Intell Res 27(1):203–233MathSciNetCrossRefGoogle Scholar
  3. Sun S, Hardoon DR (2010) Active learning with extremely sparse labeled examples. Neurocomputing 73(16–18):2980–2988CrossRefGoogle Scholar
  4. Yu S, Krishnapuram B, Rosales R, Rao RB (2011) Bayesian co-training. J Mach Learn Res 12(9):2649–2680MathSciNetzbMATHGoogle Scholar
  5. Zhang Q, Sun S (2010) Multiple-view multiple-learner active learning. Pattern Recognit 43(9):3113–3119CrossRefGoogle Scholar
  6. Zhou ZH, Chen KJ, Jiang Y (2004) Exploiting unlabeled data in content-based image retrieval. In: Proceedings of the 15th European conference on machine learning. Springer, pp 525–536Google Scholar

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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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