  In unsupervised learning, an unbiased uniform sampling strategy is typically
used, in order that the learned features faithfully encode the statistical
structure of the training data. In this work, we explore whether active example
selection strategies - algorithms that select which examples to use, based on
the current estimate of the features - can accelerate learning. Specifically,
we investigate effects of heuristic and saliency-inspired selection algorithms
on the dictionary learning task with sparse activations. We show that some
selection algorithms do improve the speed of learning, and we speculate on why
they might work.
