Optimal Subset Selection for Active Learning

Yifan Fu, Xingquan Zhu

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

2 Citations (Scopus)
9 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
CountryNew Zealand

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