Flexible Priors for Exemplar-based Clustering

  title={Flexible Priors for Exemplar-based Clustering},
  author={Daniel Tarlow and Richard S. Zemel and Brendan J. Frey},
Exemplar-based clustering methods are appealing because they offer computational benefits over latent-mean models and can handle arbitrary pairwise similarity measures between data points. However, when trying to recover underlying structure in clustering problems, tailored similarity measures are often not enough; we also desire control over the distribution of cluster sizes. Priors such as Dirichlet process priors allow the number of clusters to be unspecified while expressing priors over… CONTINUE READING
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