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Simultaneous Dimension Reduction and Clustering via the NMF-EM Algorithm

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  • Léna CAREL

    (CREST-ENSAE)

  • Pierre ALQUIER

    (CREST-ENSAE)

Abstract

Mixture models are among the most popular tools for model based clustering. However, when the dimension and the number of clusters is large, the estimation as well as the interpretation of the clusters become challenging. We propose a reduced-dimension mixture model, where the K components parameters are combinations of words from a small dictionary - say H words with H

Suggested Citation

  • Léna CAREL & Pierre ALQUIER, 2017. "Simultaneous Dimension Reduction and Clustering via the NMF-EM Algorithm," Working Papers 2017-38, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:2017-38
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