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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)

Taksonomski zapis metode · ml-model / machine-learning

Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. · DOI 10.1007/b98835

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