What is Principal Component Analysis (PCA)?
Reviewed by Jason Burns, Editorial Steward · Last updated
Principal Component Analysis (PCA) is a linear dimensionality-reduction technique that finds the orthogonal directions of greatest variance in a dataset and projects the data onto a smaller number of those directions, preserving as much variance as possible.