Nonnegative shared subspace learning and its application to social media retrieval

Sunil Kumar Gupta, Dinh Phung, Brett Adams, Truyen Tran, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

60 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationKDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data
Number of pages10
Publication statusPublished - 7 Sep 2010
Externally publishedYes
EventACM International Conference on Knowledge Discovery and Data Mining 2010 - Washington, United States of America
Duration: 24 Jul 201028 Jul 2010
Conference number: 16th

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining


ConferenceACM International Conference on Knowledge Discovery and Data Mining 2010
Abbreviated titleKDD 2010
Country/TerritoryUnited States of America
OtherProceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2010 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cup competition. KDD-2010 will run between from July 25-28 in Washington, DC and will feature hundreds of practitioners and academic data miners converging on the one location.
Internet address


  • Image and video retrieval
  • Nonnegative shared subspace learning
  • Social media
  • Transfer learning

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