AI engineering glossary · Linear algebra
What is SVD (singular value decomposition)?
Singular value decomposition (also called SVD): A factorisation of any matrix into U S V^T, where U and V are orthogonal matrices and S is diagonal with non-negative singular values in decreasing order. It exposes the matrix's most important directions and is used for compression, denoising and recommendation.
Singular value decomposition: an example
A 1000 x 300 matrix is split into U (1000 x 300), 300 singular values and V^T (300 x 300); keeping the top 20 gives a compact approximation.
Related terms
More Linear algebra terms
Where you learn it
Singular value decomposition is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.