Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most existing active learning for regression (ALR) approaches are supervised, which means the sampling process must use some label information, or an existing regression model. This paper considers completely unsupervised ALR, i.e., how to select the samples to label without knowing any true label information. We propose a novel unsupervised ALR approach, iterative representativeness-diversity maximization (iRDM), to optimally balance the representativeness and the diversity of the selected samples. Experiments on 60 datasets from various domains demonstrated its effectiveness. Our iRDM can be applied to both linear regression and kernel regression, and it even significantly outperforms supervised ALR when the number of labeled samples is small.

主动学习（AL）选择最有益的未标记样本进行标记，因此可以从相同数量的标记样本中训练出更好的机器学习模型。对大多数现有的主动回归学习（ALR）方法进行监督，这意味着采样过程必须使用一些标签信息或现有的回归模型。本文考虑了完全无监督的ALR，即如何在不知道任何真实标签信息的情况下选择要标记的样本。我们提出了一种新颖的无监督ALR方法，即迭代代表性-多样性最大化（iRDM），以最佳地平衡所选样本的代表性和多样性。对来自各个领域的60个数据集的实验证明了其有效性。我们的iRDM可以应用于线性回归和核回归，