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Principal Component Analysis Based on a Subset of Variables for Qualitative Data

  • Yuichi Mori
  • Yutaka Tanaka
  • Tomoyuki Tarumi
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

A principal component analysis based on a subset of variables is proposed by Tanaka and Mori (1996) to derive principal components which are computed as linear combinations of a subset of quantitative variables but which can reproduce all the variables very well. The present paper discusses an extension of their modified principal component analysis so that it can deal with qualitative variables by using the idea of the alternating least squares method by Young et al.(1978). A backward elimination procedure is applied to find a suitable sequence of subsets of variables. A numerical example is shown to illustrate the performance of the proposed procedure.

Keywords

Principal Component Analysis Variable Selection Instrumental Variable Variable Selection Method Optimal Scaling 
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 Japan 1998

Authors and Affiliations

  • Yuichi Mori
    • 1
  • Yutaka Tanaka
    • 2
  • Tomoyuki Tarumi
    • 2
  1. 1.Kurashiki City College160 Kojima Hieda-choKurashiki 711Japan
  2. 2.Department of Environmental and Mathematical SciencesOkayama UniversityOkayama 700Japan

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