Diversifying convex transductive experimental design for active learning

Convex Transductive Experimental Design (CTED) is one of the most representative active learning methods. It utilizes a data reconstruction framework to select informative samples for manual annotation. However, we observe that CTED cannot well handle the diversity of selected samples and hence the set of selected samples may contain mutually similar samples which convey similar or overlapped information. This is definitely undesired. Given limited budget for data labeling, it is desired to select informative samples with complementary information, i.e., similar samples are excluded. To this end, we proposes Diversified CTED by seamlessly incorporating a novel and effective diversity regularizer into CTED, ensuring the selected samples are diverse. The involvement of the diversity regularizer leads the optimization problem hard to solve. We derive an effective algorithm to solve an equivalent problem which is easier to optimize. Extensive experimental results on several benchmark data sets demonstrate that Diversified CTED significantly improves CTED and consistently outperforms the state-of-the-art methods, verifying the effectiveness and advantages of incorporating the proposed diversity regularizer into CTED.