Principal Component Analysis (PCA) Method
Principal Component Analysis (PCA) is a dimensionality reduction technique based on the Karhunen-Loève (K-L) transform. The PCA algorithm identifies an optimal linear transformation matrix W according to specific performance criteria, enabling effective reduction of high-dimensional data while preserving maximum variance.