A proposal for supervised clustering with Dirichlet Process using labels

Billy Peralta, Alberto Caro, Alvaro Soto

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Supervised clustering is an emerging area of machine learning, where the goal is to find class-uniform clusters. However, typical state-of-the-art algorithms use a fixed number of clusters. In this work, we propose a variation of a non-parametric Bayesian modeling for supervised clustering. Our approach consists of modeling the clusters as a mixture of Gaussians with the constraint of encouraging clusters of points with the same label. In order to estimate the number of clusters, we assume a-priori a countably infinite number of clusters using a variation of Dirichlet Process model over the prior distribution. In our experiments, we show that our technique typically outperforms the results of other clustering techniques.

Original languageEnglish
Pages (from-to)52-57
Number of pages6
JournalPattern Recognition Letters
Publication statusPublished - 1 Sept 2016


  • Clustering
  • Dirichlet Process
  • Supervised clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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