A Caltech Library Service

Adaptively Learning the Crowd Kernel

Tamuz, Omer and Liu, Ce and Belongie, Serge and Shamir, Ohad and Kalai, Adam Tauman (2011) Adaptively Learning the Crowd Kernel. In: International Conference on Machine Learning. ICML. ACM , New York, NY, pp. 673-680. ISBN 978-1-4503-0619-5.

[img] PDF - Published Version
See Usage Policy.

[img] PDF - Submitted Version
See Usage Policy.


Use this Persistent URL to link to this item:


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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Paper
Tamuz, Omer0000-0002-0111-0418
Belongie, Serge0000-0002-0388-5217
Additional Information:© 2011 by the author(s)/owner(s). We thank Sham Kakade and Varun Kanade for helpful discussions. Serge Belongie's research is partly funded by ONR MURI Grant N00014-08-1-0638 and NSF Grant AGS-0941760.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-08-1-0638
Series Name:ICML
Record Number:CaltechAUTHORS:20161110-102619803
Persistent URL:
Official Citation:Omer Tamuz and Ce Liu and Serge Belongie and Ohad Shamir and Adam Kalai Adaptively Learning the Crowd Kernel Proceedings of the 28th International Conference on Machine Learning series ICML '11, editor Lise Getoor and Tobias Scheffer Bellevue, Washington, USA, 978-1-4503-0619-5 publisher ACM, New York, NY, USA, pages 673-680.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:71912
Deposited By: Ruth Sustaita
Deposited On:10 Nov 2016 22:47
Last Modified:09 Mar 2020 13:18

Repository Staff Only: item control page