# Principal Component Analysis

> Source: https://sukruyusufkaya.com/en/glossary/principal-component-analysis
> Updated: 2026-05-13T20:02:45.692Z
> Type: glossary
> Category: makine-ogrenmesi
**TLDR:** The most widely used linear dimensionality reduction method that preserves most of the variance in the data.

<p>Principal Component Analysis reduces high-dimensional data into a smaller number of components while trying to minimize information loss. It builds a new feature space by selecting the directions that carry the greatest variance. It is widely used for visualization, noise reduction, data compression, and preprocessing before modeling. However, since it is a linear method, it may not fully preserve complex nonlinear structures.</p>