Multiview Active Learning

Abstract

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.