Principal component analysis: Difference between revisions

Revision as of 02:19, 10 May 2025

Revision as of 06:44, 17 June 2025

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{{Short description|Method of data analysis}}

[[File:GaussianScatterPCA.svg|thumb|upright=1.3|PCA of a [[multivariate Gaussian distribution]] centered at (1, 3) with a standard deviation of 3 in roughly the (0.866, 0.5) direction and of 1 in the orthogonal direction. The vectors shown are the [[Eigenvalues and eigenvectors|eigenvectors]] of the [[covariance matrix]] scaled by the square root of the corresponding eigenvalue, and shifted so their tails are at the mean.]]

{{Machine learning bar}}

'''Principal component analysis''' ('''PCA''') is a [[Linear map|linear]] [[dimensionality reduction]] technique with applications in [[exploratory data analysis]], visualization and [[Data Preprocessing|data preprocessing]].

The data is [[linear map|linearly transformed]] onto a new [[coordinate system]] such that the directions (principal components) capturing the largest variation in the data can be easily identified.