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Online Variational Learning of Dirichlet Process Mixtures of Scaled Dirichlet Distributions

Author

Listed:
  • Narges Manouchehri

    (Concordia University)

  • Hieu Nguyen

    (Concordia University)

  • Pantea Koochemeshkian

    (Concordia University)

  • Nizar Bouguila

    (Concordia University)

  • Wentao Fan

    (Huaqiao University)

Abstract

Data clustering as an unsupervised method has been one of the main attention-grabbing techniques and a large class of tasks can be formulated by this method. Mixture models as a branch of clustering methods have been used in various fields of research such as computer vision and pattern recognition. To apply these models, we need to address some problems such as finding a proper distribution that properly fits data, defining model complexity and estimating the model parameters. In this paper, we apply scaled Dirichlet distribution to tackle the first challenge and propose a novel online variational method to mitigate the other two issues simultaneously. The effectiveness of the proposed work is evaluated by four challenging real applications, namely, text and image spam categorization, diabetes and hepatitis detection.

Suggested Citation

  • Narges Manouchehri & Hieu Nguyen & Pantea Koochemeshkian & Nizar Bouguila & Wentao Fan, 2020. "Online Variational Learning of Dirichlet Process Mixtures of Scaled Dirichlet Distributions," Information Systems Frontiers, Springer, vol. 22(5), pages 1085-1093, October.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10027-2
    DOI: 10.1007/s10796-020-10027-2
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    Cited by:

    1. Lydia Bouzar-Benlabiod & Stuart H. Rubin, 2020. "Heuristic Acquisition for Data Science," Information Systems Frontiers, Springer, vol. 22(5), pages 1001-1007, October.
    2. Meihua Zuo & Spyros Angelopoulos & Zhouyang Liang & Carol X. J. Ou, 2023. "Blazing the Trail: Considering Browsing Path Dependence in Online Service Response Strategy," Information Systems Frontiers, Springer, vol. 25(4), pages 1605-1619, August.

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