Groups
Category
Singular Value Decomposition (SVD) factors any m×n matrix A into A = UΣV^{T}, where U and V are orthogonal and Σ is diagonal with nonnegative entries.
Eigenvalue decomposition rewrites a square matrix as a change of basis that reveals how it stretches and rotates space.