AI Glossary

Principal component analysis

Also called PCA

What does “Principal component analysis” mean?

Principal component analysis: A linear transformation that finds orthogonal directions capturing the most variance in data.

Principal component analysis visual guide: A linear transformation that finds orthogonal directions capturing the most variance in data.
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What is an example of “Principal component analysis”?

An analyst projects many sensor features onto two components to inspect patterns.

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See also

ClassificationTermRegressionTermDecision treeTermK-means clusteringTermK-nearest neighborsTerm

Same topic

ProbabilityTermPredictionTermPromptTermParametersTermRandom forestTerm