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    Model-based classification and novelty detection for point pattern data

    Access Status
    Fulltext not available
    Authors
    Vo, Ba Tuong
    Tran, N.
    Phung, D.
    Vo, Ba-Ngu
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Vo, B.T. and Tran, N. and Phung, D. and Vo, B. 2017. Model-based classification and novelty detection for point pattern data, pp. 2622-2627.
    Source Title
    Proceedings - International Conference on Pattern Recognition
    DOI
    10.1109/ICPR.2016.7900030
    ISBN
    9781509048472
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/56163
    Collection
    • Curtin Research Publications
    Abstract

    © 2016 IEEE. Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern data via random finite sets (RFS). In particular, we propose appropriate likelihood functions, and a maximum likelihood estimator for learning a tractable family of RFS models. In novelty detection, we propose novel ranking functions based on RFS models, which substantially improve performance.

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