« Back to Glossary Index

A statistical technique that uses an orthogonal transformation to convert a set of potentially correlated variables into a set of linearly uncorrelated variables known as principal components. These principal components capture the most significant variance in the data, allowing for dimensionality reduction and highlighting the most important features for analysis. PCA is widely used for data preprocessing, feature extraction, and visualization.