Principal component analysis (linear algebra perspective)

Principal Component Analysis (PCA) is a dimensionality reduction technique with foundations in linear algebra, particularly eigendecomposition.

For comprehensive coverage of this topic, see: Principal Component Analysis

Linear Algebra Connection

PCA relies on several core linear algebra concepts:

• Covariance matrix calculation and properties
• Eigenvalues and eigenvectors for determining principal components
• Orthogonal transformations
• Matrix decomposition (specifically related to SVD)

These concepts provide the mathematical foundation upon which the numerical implementations of PCA are built.