  In many real-world machine learning applications, unlabeled data are abundant
whereas class labels are expensive and scarce. An active learner aims to obtain
a model of high accuracy with as few labeled instances as possible by
effectively selecting useful examples for labeling. We propose a new selection
criterion that is based on statistical leverage scores and present two novel
active learning methods based on this criterion: ALEVS for querying single
example at each iteration and DBALEVS for querying a batch of examples. To
assess the representativeness of the examples in the pool, ALEVS and DBALEVS
use the statistical leverage scores of the kernel matrices computed on the
examples of each class. Additionally, DBALEVS selects a diverse a set of
examples that are highly representative but are dissimilar to already labeled
examples through maximizing a submodular set function defined with the
statistical leverage scores and the kernel matrix computed on the pool of the
examples. The submodularity property of the set scoring function let us
identify batches with a constant factor approximate to the optimal batch in an
efficient manner. Our experiments on diverse datasets show that querying based
on leverage scores is a powerful strategy for active learning.
