这篇论文评估了Greedy K-center在主动学习选择中的性能，对于对主动学习感兴趣的人来说是个不错的参考。
本文评估了Greedy K-center在多种度量空间中的性能，包括原始特征空间、线性判别分析空间和模型推导概率空间。实验结果表明，将未标记实例映射到预测概率空间并按熵加权通常优于其他选项。
Diversity-Based Active Learning: An Evaluation of Metric Spaces for Active Learning Selection
With rapid advancement over the last few years, many different methods are now widely used for classification. However, training these models requires substantial labeled data. Active Learning is a potential solution to this problem. Pool-based active learning minimizes costs by querying only the most informative samples from an unlabeled dataset. Diversity-based approaches, on the other hand, attempt to select a representative subset of the data. There are many different objectives for determining the selection process, including exact K-center, exact K-median, and Greedy K-center. In this paper, we will focus on evaluating the performance of Greedy K-center across a variety of metric spaces: the raw feature space, a Linear Discriminant Analysis (LDA) space, and a model-derived probability space (with and without entropy-based weighting). Using Random Forest classifiers as a baseline evaluator, our empirical results on synthetic and real-world datasets demonstrate that mapping unlabeled instances into a predictive probability space and weighting the result by entropy often dominates the other options for active learning selection with Greedy K-center.