Title: A Variance Maximization Criterion for Active Learning,
Abstract: Active learning aims to train a classifier as fast as possible with as few
labels as possible. The core element in virtually any active learning strategy
is the criterion that measures the usefulness of the unlabeled data based on
which new points to be labeled are picked. We propose a novel approach which we
refer to as maximizing variance for active learning or MVAL for short. MVAL
measures the value of unlabeled instances by evaluating the rate of change of
output variables caused by changes in the next sample to be queried and its
potential labelling. In a sense, this criterion measures how unstable the
classifier's output is for the unlabeled data points under perturbations of the
training data. MVAL maintains, what we refer to as, retraining information
matrices to keep track of these output scores and exploits two kinds of
variance to measure the informativeness and representativeness, respectively.
By fusing these variances, MVAL is able to select the instances which are both
informative and representative. We employ our technique both in combination
with logistic regression and support vector machines and demonstrate that MVAL
achieves state-of-the-art performance in experiments on a large number of
standard benchmark datasets.