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From Data and Information Analysis to Knowledge Engineering pp 294–301Cite as

Hausman Principal Component Analysis

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Abstract

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.

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Author information

Authors and Affiliations

  1. D’ept. de Math/Statistique, Université de Moncton, Moncton, NB, Canada, E1A 3E9

    Vartan Choulakian

  2. Dept. of Mathematics and Statistics, University of Naples “Federico II”, 80126, Napoli, Italy

    Luigi Dambra & Biagio Simonetti

Authors
  1. Vartan Choulakian
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  2. Luigi Dambra
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  3. Biagio Simonetti
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Editor information

Editors and Affiliations

  1. Institut für Technische und Betriebliche Informationssysteme, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Myra Spiliopoulou

  2. Institut für Wissens- und Sprachverarbeitung, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Rudolf Kruse , Christian Borgelt  & Andreas Nürnberger ,  & 

  3. Institut für Entscheidungstheorie und Unternehmensforschung, Universität Karlsruhe (TH), 76128, Karlsruhe

    Wolfgang Gaul

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© 2006 Springer Berlin · Heidelberg

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Choulakian, V., Dambra, L., Simonetti, B. (2006). Hausman Principal Component Analysis. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_35

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