Adaptively Learning the Crowd Kernel

Omer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, Adam Kalai

Proceedings of the 28th International Conference on Machine Learning (ICML), 2011 |

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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.” SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as ” is striped” among neckties and “vowel vs. consonant” among letters.