Science China Information Sciences

, Volume 59, Issue 1, pp 1–14 | Cite as

Unsupervised learning of Dirichlet process mixture models with missing data

  • Xunan Zhang
  • Shiji SongEmail author
  • Lei Zhu
  • Keyou You
  • Cheng Wu
Research Paper


This study presents a novel approach to unsupervised learning for clustering with missing data. We first extend a finite mixture model to the infinite case by considering Dirichlet process mixtures, which can automatically determine the number of mixture components or clusters. Furthermore, we view the missing features as latent variables and compute the posterior distributions using the variational Bayesian expectation maximization algorithm, which optimizes the evidence lower bound on the complete-data log marginal likelihood. We demonstrate the performance on several artificial data sets with missing values. The experimental results indicate that the proposed method outperforms some classic imputation methods. We finally present an application to seabed hydrothermal sulfide color images analysis problem.


Dirichlet processes missing data clustering variational Bayesian image analysis 





Dirichlet过程 缺失数据 聚类 变分贝叶斯 图像分析 


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Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Xunan Zhang
    • 1
  • Shiji Song
    • 1
    Email author
  • Lei Zhu
    • 2
  • Keyou You
    • 1
  • Cheng Wu
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina
  2. 2.China Ocean Mineral Resources R&D AssociationBeijingChina

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