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From Dirichlet Process mixture models to spectral clustering

Author

Listed:
  • Stefano Tonellato

    (Department of Economics, University Of Venice CÃ Foscari)

Abstract

This paper proposes a clustering method based on the sequential estimation of the random partition induced by the Dirichlet process. Our approach relies on the Sequential Importance Resampling (SIR) algorithm and on the estimation of the posterior probabilities that each pair of observations are generated by the same mixture component. Such estimates do not require the identification of mixture components, and therefore are not affected by label switching. Then, a similarity matrix can be easily built, allowing for the construction of a weighted undirected graph, where nodes represent individuals and edge weights quantify the similarity between pairs of individuals. The paper shows how, in such a context, spectral clustering techniques can be applied in order to identify homogeneous groups.

Suggested Citation

  • Stefano Tonellato, 2017. "From Dirichlet Process mixture models to spectral clustering," Working Papers 2017:33, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2017:33
    as

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    References listed on IDEAS

    as
    1. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    2. Raftery, Adrian E. & Dean, Nema, 2006. "Variable Selection for Model-Based Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 168-178, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Dirichlet process priors; sampling importance resampling; weighted graph; laplacian; spectral clustering;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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