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.