Adaptively Learning the Crowd Kernel

We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 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.

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Omer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, Adam Tauman Kalai
• Keywords: Algorithm Samples, CORR 2011, Crowd Kernel, Education, Similarity Matrix