Hausman Principal Component Analysis

  • Vartan Choulakian
  • Luigi Dambra
  • Biagio Simonetti
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


The aim of this paper is to obtain discrete-valued weights of the variables by constraining them to Hausman weights (−1, 0, 1) in principal component analysis. And this is done in two steps: First, we start with the centroid method, which produces the most restricted optimal weights −1 and 1; then extend the weights to −1,0 or 1.


Principal Component Analysis Centroid Method Multiple Factor Analysis Ordinary Principal Component Analysis Principal Component Analysis Loading 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Vartan Choulakian
    • 1
  • Luigi Dambra
    • 2
  • Biagio Simonetti
    • 2
  1. 1.D’ept. de Math/StatistiqueUniversité de MonctonMonctonCanada
  2. 2.Dept. of Mathematics and StatisticsUniversity of Naples “Federico II”NapoliItaly

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