What Does PCA Stand For?

PCA stands for Principal Component Analysis

Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction and data analysis. It transforms a high-dimensional dataset into a lower-dimensional form while preserving as much variance as possible. PCA identifies the principal components, which are linear combinations of the original variables, highlighting the directions in which the data varies the most. This method is widely applied in fields such as machine learning, image processing, and exploratory data analysis to simplify complex datasets, enhance visualization, and improve model performance.

Other Meanings for PCA