  In many real-world machine learning applications, unlabeled data can be
easily obtained, but it is very time-consuming and/or expensive to label them.
So, it is desirable to be able to select the optimal samples to label, so that
a good machine learning model can be trained from a minimum amount of labeled
data. Active learning (AL) has been widely used for this purpose. However, most
existing AL approaches are supervised: they train an initial model from a small
amount of labeled samples, query new samples based on the model, and then
update the model iteratively. Few of them have considered the completely
unsupervised AL problem, i.e., starting from zero, how to optimally select the
very first few samples to label, without knowing any label information at all.
This problem is very challenging, as no label information can be utilized. This
paper studies unsupervised pool-based AL for linear regression problems. We
propose a novel AL approach that considers simultaneously the informativeness,
representativeness, and diversity, three essential criteria in AL. Extensive
experiments on 14 datasets from various application domains, using three
different linear regression models (ridge regression, LASSO, and linear support
vector regression), demonstrated the effectiveness of our proposed approach.
