How does PCA relate to spectral decomposition in linear algebra?

PCA is equivalent to spectral decomposition of the covariance matrix.

Spectral decomposition expresses a matrix as eigenvectors and eigenvalues. PCA applies this to covariance matrix to find orthogonal directions of maximum variance. This connects PCA directly to linear algebra foundations and ensures orthogonality of components.