  Active learning aims to obtain a classifier of high accuracy by using fewer
label requests in comparison to passive learning by selecting effective
queries. Many active learning methods have been developed in the past two
decades, which sample queries based on informativeness or representativeness of
unlabeled data points. In this work, we explore a novel querying criterion
based on statistical leverage scores. The statistical leverage scores of a row
in a matrix are the squared row-norms of the matrix containing its (top) left
singular vectors and is a measure of influence of the row on the matrix.
Leverage scores have been used for detecting high influential points in
regression diagnostics and have been recently shown to be useful for data
analysis and randomized low-rank matrix approximation algorithms. We explore
how sampling data instances with high statistical leverage scores perform in
active learning. Our empirical comparison on several binary classification
datasets indicate that querying high leverage points is an effective strategy.
