Active Learning with Multiple Views

Abstract

Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing.

References in corpus (2)

• Wrapper Maintenance: A Machine Learning Approach
• Committee-Based Sample Selection for Probabilistic Classifiers

Cited by in corpus (9)

• A Survey on Multi-view Learning
• A survey of active learning algorithms for supervised remote sensing image classification
• Multimodal Hierarchical Dirichlet Process-based Active Perception
• From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research
• Co-Learning Bayesian Optimization
• A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria
• Adversarial Attacks for Multi-view Deep Models
• Unsupervised Co-Learning on $\mathcal{G}$-Manifolds Across Irreducible Representations
• Multi-View Intact Space Learning