An online classification EM algorithm based on the mixture model

详细信息    查看全文

• 作者：Allou Samé ; Christophe Ambroise and Gérard Govaert
• 关键词：Clustering ; Mixture model ; EM ; CEM ; Stochastic gradient ; Exponential family
• 刊名：Statistics and Computing
• 出版年：2007
• 出版时间：September, 2007
• 年：2007
• 卷：17
• 期：3
• 页码：209-218
• 全文大小：735 KB
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics Computing and Software
  Statistics
  Numeric Computing
  Mathematics
  Artificial Intelligence and Robotics
  
• 出版者：Springer Netherlands
• ISSN：1573-1375

文摘

Mixture model-based clustering is widely used in many applications. In certain real-time applications the rapid increase of data size with time makes classical clustering algorithms too slow. An online clustering algorithm based on mixture models is presented in the context of a real-time flaw-diagnosis application for pressurized containers which uses data from acoustic emission signals. The proposed algorithm is a stochastic gradient algorithm derived from the classification version of the EM algorithm (CEM). It provides a model-based generalization of the well-known online k-means algorithm, able to handle non-spherical clusters. Using synthetic and real data sets, the proposed algorithm is compared with the batch CEM algorithm and the online EM algorithm. The three approaches generate comparable solutions in terms of the resulting partition when clusters are relatively well separated, but online algorithms become faster as the size of the available observations increases.