We introduce an algorithm that, given n objects, learns a similarity matrix
over all n^2 pairs, from crowdsourced data alone. The algorithm samples
responses to adaptively chosen triplet-based relative-similarity queries. Each
query has the form "is object 'a' more similar to 'b' or to 'c'?" and is chosen
to be maximally informative given the preceding responses. The output is an
embedding of the objects into Euclidean space (like MDS); we refer to this as
the "crowd kernel."