<P> 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 positive semidefinite normal matrix ( for example , a symmetric matrix with positive eigenvalues ) to any m × n ( \ displaystyle m \ times n ) matrix via an extension of the polar decomposition . It has many useful applications in signal processing and statistics . </P>
<P> 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 positive semidefinite normal matrix ( for example , a symmetric matrix with positive eigenvalues ) to any m × n ( \ displaystyle m \ times n ) matrix via an extension of the polar decomposition . It has many useful applications in signal processing and statistics . </P>
what is singular value decomposition of a matrix
what is singular value decomposition of a matrix
the generalization of the eigendecomposition of a positive semidefinite normal matrix ( for example , a symmetric matrix with positive eigenvalues ) to any m × n ( \ displaystyle m \ times n ) matrix via an extension of the polar decomposition
the generalization of the eigendecomposition of a positive semidefinite normal matrix ( for example , a symmetric matrix with positive eigenvalues ) to any m × n ( \ displaystyle m \ times n ) matrix via an extension of the polar decomposition