Promoting active learning with mixtures of Gaussian processes

Active learning is an effective methodology to relieve the tedious and expensive work of manual annotation for many supervised learning applications. The active learning framework with good performance usually contains powerful learning models and delicate active learning strategies. Gaussian process (GP)-based active learning was proposed to be one of the most effective methods. However, the single GP suffers from the limitation of not modeling multimodal data well enough, and thus existing active learning strategies based on GPs only make use of limited information from data. In this paper, we propose three novel active learning methods, in which the existing mixture of GP model (MGP) is adjusted as the learning model and three active learning strategies are designed based on the adjusted MGP. Through experiments on multiple data sets, we analyze the performance and characteristics of the three proposed active learning methods, and further compare with popular GP-based methods and some other state-of-the-art methods.