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Interesting Projections of Multidimensional Data by Means of Generalized Principal Component Analyses

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Compstat

Summary

Principal Component Analysis can produce several interesting projections of a point cloud if suitable inner products are chosen for measuring the distances between the units. We discuss two examples of such choices. The first one allows us to display outliers, while the second is expected to display clusters. Doing so we introduce a robust estimate of a covariance matrix and we investigate some of its properties.

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References

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Author information

Authors and Affiliations

  1. Toulouse, France

    H. Caussinus & A. Ruiz

Authors
  1. H. Caussinus
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  2. A. Ruiz
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Editor information

Editors and Affiliations

  1. University of Zagreb, Engelsova bb, 41000, Zagreb, Yugoslavia

    Konstantin Momirović  & Vesna Mildner MA  & 

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© 1990 Physica-Verlag Heidelberg

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Caussinus, H., Ruiz, A. (1990). Interesting Projections of Multidimensional Data by Means of Generalized Principal Component Analyses. In: Momirović, K., Mildner, V. (eds) Compstat. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-50096-1_19

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  • DOI: https://doi.org/10.1007/978-3-642-50096-1_19

  • Publisher Name: Physica-Verlag HD

  • Print ISBN: 978-3-7908-0475-1

  • Online ISBN: 978-3-642-50096-1

  • eBook Packages: Springer Book Archive

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