A Rotated Principal Component Analysis for an Advanced Dimension Reduction Approach

Alex Karagrigoriou (), Christos E. Kountzakis () and Kimon Ntotsis ()
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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

Chapter Chapter 5 in Quantitative Methods and Data Analysis in Applied Demography - Volume 2, 2025, pp 39-49 from Springer

Abstract: 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.

Keywords: Dimension reduction; Principal component analysis; Factor analysis; Principal rotation analysis (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-3-031-82279-7_5

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DOI: 10.1007/978-3-031-82279-7_5

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