Machine learning
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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Sources
- Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI: 10.1007/b98835 ↗
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Referenced by
AutoencoderBayesian Factor AnalysisCFACFA — Scale ValidationCronbach's AlphaDifferential EvolutionDiffusion ModelEFAEFA for Scale DevelopmentGaussian Mixture ModelHierarchical ClusteringIsolation ForestIsomapK-meansLasso RegressionLinear Discriminant Analysis (Classification)Ridge RegressionRobust Factor AnalysisRobust PCAScore-Based Generative ModelSpectral ClusteringStochastic Block Modelt-SNEUMAPVariational Autoencoder