Nonnegative shared subspace learning and its application to social media retrieval

Gupta, Sunil Kumar, Phung, Dinh, Adams, Brett, Tran, Truyen and Venkatesh, Svetha 2010, Nonnegative shared subspace learning and its application to social media retrieval, in KDD 2010 : Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, IEEE, New York, N. Y., pp. 1169-1178.

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Title Nonnegative shared subspace learning and its application to social media retrieval
Author(s) Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Adams, Brett
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Knowledge Discovery and Data Mining (16th : 2010 : Washington, D. C.)
Conference location Washington, D. C.
Conference dates 25-28 Jul. 2010
Title of proceedings KDD 2010 : Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Editor(s) [Unknown]
Publication date 2010
Conference series International Conference on Knowledge Discovery and Data Mining
Start page 1169
End page 1178
Total pages 10
Publisher IEEE
Place of publication New York, N. Y.
Keyword(s) image and video retrieval
nonnegative shared subspace learning
social media
transfer learning
Summary Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can seriously affect the precision of a social tag-based web retrieval system. Therefore improving the precision performance of these social tag-based web retrieval systems has become an increasingly important research topic. To this end, we propose a shared subspace learning framework to leverage a secondary source to improve retrieval performance from a primary dataset. This is achieved by learning a shared subspace between the two sources under a joint Nonnegative Matrix Factorization in which the level of subspace sharing can be explicitly controlled. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. We validate the framework on image and video retrieval tasks in which tags from the LabelMe dataset are used to improve image retrieval performance from a Flickr dataset and video retrieval performance from a YouTube dataset. This has implications for how to exploit and transfer knowledge from readily available auxiliary tagging resources to improve another social web retrieval system. Our shared subspace learning framework is applicable to a range of problems where one needs to exploit the strengths existing among multiple and heterogeneous datasets.
ISBN 9781450300551
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044538

Document type: Conference Paper
Collection: School of Information Technology
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