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. Attached Files Name Description MIMEType Size Downloads Title Nonnegative shared subspace learning and its application to social media retrieval Author(s) Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930 Phung, Dinh orcid.org/0000-0002-9977-8247 Adams, Brett Tran, Truyen orcid.org/0000-0001-6531-8907 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 Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 20 Apr 2012, 11:30:47 EST Tue, 24 Apr 2012, 15:21:44 EST Tue, 24 Apr 2012, 15:23:31 EST Tue, 23 Feb 2016, 14:01:24 EST Thu, 03 Mar 2016, 12:56:29 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 50 times in Scopus Search Google Scholar Access Statistics: 408 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:30:47 EST

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