Active model selection for Graph-Based Semi-Supervised Learning

The recent years have witnessed a surge of interest in graph-based semi-supervised learning (GBSSL). However, despite its extensive research, there has been little work on graph construction, which is at the heart of GBSSL. In this study, we propose a novel active learning method, active model selection (AMS), which aims at learning both data labels and the optimal graph by allowing the learner the flexibility to choose samples for labeling. AMS minimizes the regularization function in GBSSL by iterating between the active sample selection step and the graph reconstruction step, where the samples querying which leads to the optimal graph are selected. Experimental results on four real-world datasets are provided to demonstrate the effectiveness of AMS.