About this document
Matrix Rank Approximation via SVD by edwarzambrano is a document available to read on EtoBox.
The document discusses singular value decomposition (SVD) of real matrices. SVD expresses a matrix A as the product of three matrices: A = UΣV^T, where U and V are orthogonal matrices and Σ is a diagonal matrix of singular values. SVD has numerous applications including computation of fundamental subspaces, polar decomposition, least squares approximation, data compression, and matrix approximation. A brief history of SVD is also provided.
- Author
- edwarzambrano
- Language
- EN