  We introduce an unsupervised approach to efficiently discover the underlying
features in a data set via crowdsourcing. Our queries ask crowd members to
articulate a feature common to two out of three displayed examples. In addition
we also ask the crowd to provide binary labels to the remaining examples based
on the discovered features. The triples are chosen adaptively based on the
labels of the previously discovered features on the data set. In two natural
models of features, hierarchical and independent, we show that a simple
adaptive algorithm, using "two-out-of-three" similarity queries, recovers all
features with less labor than any nonadaptive algorithm. Experimental results
validate the theoretical findings.
