PCA Weighting — Principal Component Analysis based objective weighting
PCA-WEIGHT (PCA Weighting — Principal Component Analysis based objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pearson, K. in 1901. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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Method map
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When to use it
PCA weighting extracts weights from the structure of the data itself — criteria that explain more variance (are less redundant) get higher weight. Requires m ≥ n for a non-singular covariance matrix. Results can be sensitive to dataset changes (new alternatives shift weights).
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- Results depend on the chosen normalisation, weights, and parameter settings.
Sources
- Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine DOI: 10.1080/14786440109462720 ↗
How to cite this page
ScholarGate. (2026, June 2). PCA Weighting — Principal Component Analysis based objective weighting. ScholarGate. https://scholargate.app/en/decision-making/pca-weight
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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