Question

Singular value decomposition of a matrix linear algebra

Answer 1

Step-by-step explanation:

In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It is the generalization of the eigendecomposition of a normal matrix (for example, a symmetric matrix with non-negative eigenvalues) to any matrix via an extension of the polar decomposition.

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Answer 2

Answer:

Singular Value Decomposition (SVD) is another type of decomposition. Unlike eigendecomposition where the matrix you want to decompose has to be a square matrix, SVD allows you to decompose a rectangular matrix (a matrix that has different numbers of rows and columns). ... And the middle matrix is a diagonal matrix.