Adaptively learning the crowd kernel
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
---|---|
Journal | Proceedings of the 28th International Conference on Machine Learning, ICML 2011 |
Pages (from-to) | 673-680 |
Number of pages | 8 |
Publication status | Published - 2011 |
Externally published | Yes |
Event | 28th International Conference on Machine Learning, ICML 2011 - Bellevue, WA, United States Duration: 28 Jun 2011 → 2 Jul 2011 |
Conference
Conference | 28th International Conference on Machine Learning, ICML 2011 |
---|---|
Country | United States |
City | Bellevue, WA |
Period | 28/06/2011 → 02/07/2011 |
Sponsor | Amazon.com, Inc. (Biz), NSF, Microsoft, Google, Yahoo! Labs |
ID: 301831017