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Principal Component Analysis/Evidence
Method evidence record

Principal Component Analysis

Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.

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Principal Component Analysis (PCA)
Taxonomic method record · ml-model / machine-learning
  • Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. · DOI 10.1007/b98835
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

See alsoFactor Analysismachine-suggested · Relational suggestion, not evidence.Same method familyHierarchical Clusteringmachine-suggested · Relational suggestion, not evidence.Same method familyLasso Regressionmachine-suggested · Relational suggestion, not evidence.

Evidence status

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Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

1 recorded citation, copied from the method source record.

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