Principal Component Analysis

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.