Optimal Subset Selection for Active Learning

Yifan Fu, Xingquan Zhu

    Research output: Book chapter/Published conference paperConference paperpeer-review

    2 Citations (Scopus)
    21 Downloads (Pure)


    Active learning traditionally relies on instance based utility measures to rank and select instances for labeling, which may result in labeling redundancy. To address this issue, we explore instance utility from twodimensions: individual uncertainty and instance disparity, using a correlation matrix. The active learning is transformed to a semi-definite programming problem toselect an optimal subset with maximum utility value. Experiments demonstrate the algorithm performance incomparison with baseline approaches.
    Original languageEnglish
    Title of host publicationAAAI
    Place of PublicationUnited States
    PublisherAAAI Press
    Number of pages2
    Publication statusPublished - 2011
    EventAAAI Conference on Artificial Intelligence - San Francisco, California, USA, New Zealand
    Duration: 07 Aug 201111 Aug 2011


    ConferenceAAAI Conference on Artificial Intelligence
    Country/TerritoryNew Zealand


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