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A Rotated Principal Component Analysis for an Advanced Dimension Reduction Approach

In: Quantitative Methods and Data Analysis in Applied Demography - Volume 2

Author

Listed:
  • Alex Karagrigoriou

    (University of Piraeus and Graphic Era Deemed to be University, Department of Statistics and Insurance Science)

  • Christos E. Kountzakis

    (University of the Aegean, Department of Statistics and Actuarial-Financial Mathematics)

  • Kimon Ntotsis

    (NIHR Leicester Biomedical Research Centre, University of Leicester)

Abstract

The problem of dimension reduction is a popular problem that arises in practice and it is usually combined with the concept of collinearity. In this work we proceed with a proposal of a new dimension reduction technique that can be considered as a rotated Principal Component Analysis, to be referred to as, Principal Rotation Analysis (PRA). With the use of this technique, we can generate independent features that each encloses the variability of the original features and simultaneously create clusters of significance.

Suggested Citation

  • Alex Karagrigoriou & Christos E. Kountzakis & Kimon Ntotsis, 2025. "A Rotated Principal Component Analysis for an Advanced Dimension Reduction Approach," The Springer Series on Demographic Methods and Population Analysis, in: Christos H. Skiadas & Charilaos Skiadas (ed.), Quantitative Methods and Data Analysis in Applied Demography - Volume 2, chapter 0, pages 39-49, Springer.
    • Handle: RePEc:spr:ssdmcp:978-3-031-82279-7_5
    DOI: 10.1007/978-3-031-82279-7_5
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