Active class discovery by querying pairwise label homogeneity

Yifan Fu, Junbin Gao, Xingquan Zhu

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


    Active learning traditionally focuses on labeling the most informative instances for some well defined learning tasks with known class labels, and a labeler is provided to label each queried instance. In an extreme case, the whole active learning task may start without any available information about the tasks, for instance, no labeled data are available at the initial stage and the labeler is incapable of providing the ground truth to each queried instance. In this paper, we propose an active class discovery method for the case where no randomly labeled instances exist to kick-off the learning circle and the labeler only has weak knowledge to answer whether a pair of instances belong to the same class or not. To roughly identify the classes in the data, a Minimum Spanning Tree based query strategy is employed to discover a number of classes from unlabeled data. Experiments and comparisons demonstrate superior performance of the proposed method for class discovery tasks.
    Original languageEnglish
    Title of host publicationICDS 2015
    Place of PublicationSwitzerland
    Number of pages6
    ISBN (Electronic)9783319244730
    Publication statusPublished - 2015
    EventInternational Conference on Data Science - University of Technology Sydney , Sydney, Australia
    Duration: 08 Aug 201509 Aug 2015 (Conference proceedings)


    ConferenceInternational Conference on Data Science
    Internet address


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