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Soft Computing

A principal component analysis based method for the priority vector derivation from pairwise comparison matrices

  • 31-10-2025
  • Soft computing in decision making and in modeling in economics
Published in:
Authors
Shihui Wu
Huachun Xiang
Bo He
Xiaodong Liu
Published in
Soft Computing | Issue 23-24/2025

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Abstract

The article introduces a novel method called Principal Component Analysis based Method (PCAM) for deriving priority vectors from pairwise comparison matrices (PCMs) in the Analytic Hierarchy Process (AHP). The method leverages the first principal component from PCA to represent the priority vector, offering a new consistency index called the Principal Component Index (PCI). The article provides a comprehensive comparison of PCAM with other prioritization methods, demonstrating its effectiveness and reliability. It also discusses the practical applications and advantages of PCAM, highlighting its potential as a compelling alternative to other prioritization approaches. The text concludes with a discussion on the limitations of PCAM and future research directions.
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Title
A principal component analysis based method for the priority vector derivation from pairwise comparison matrices
Authors
Shihui Wu
Huachun Xiang
Bo He
Xiaodong Liu
Publication date
31-10-2025
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 23-24/2025
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-025-10882-8
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